├── .gitignore ├── LICENSE.txt ├── MANIFEST.in ├── Predictor.ipynb ├── README.md ├── liberty ├── 1st_version │ ├── All_sides_moral_analysis.ipynb │ ├── Reason_moral_analysis.ipynb │ └── lexicon_filtered.tsv └── 2nd_version │ ├── cs_lexicon_final.csv │ └── we_lexicon_final.csv ├── moralstrength ├── __init__.py ├── annotations │ ├── authority.tsv │ ├── care.tsv │ ├── fairness.tsv │ ├── loyalty.tsv │ ├── purity.tsv │ └── v1.1 │ │ ├── authority.tsv │ │ ├── care.tsv │ │ ├── fairness.tsv │ │ ├── lexicons_expanded_v1-1.pck │ │ ├── loyalty.tsv │ │ └── purity.tsv ├── data.py ├── estimators.py ├── export │ ├── count_authority_lr.pck │ ├── count_care_lr.pck │ ├── count_fairness_lr.pck │ ├── count_loyalty_lr.pck │ ├── count_non-moral_lr.pck │ ├── count_purity_lr.pck │ ├── freq_authority_lr.pck │ ├── freq_care_lr.pck │ ├── freq_fairness_lr.pck │ ├── freq_loyalty_lr.pck │ ├── freq_non-moral_lr.pck │ ├── freq_purity_lr.pck │ ├── ngram.pck │ ├── unigram+count+freq_authority_lr.pck │ ├── unigram+count+freq_care_lr.pck │ ├── unigram+count+freq_fairness_lr.pck │ ├── unigram+count+freq_loyalty_lr.pck │ ├── unigram+count+freq_non-moral_lr.pck │ ├── unigram+count+freq_purity_lr.pck │ ├── unigram+count_authority_lr.pck │ ├── unigram+count_care_lr.pck │ ├── unigram+count_fairness_lr.pck │ ├── unigram+count_loyalty_lr.pck │ ├── unigram+count_non-moral_lr.pck │ ├── unigram+count_purity_lr.pck │ ├── unigram+freq_authority_lr.pck │ ├── unigram+freq_care_lr.pck │ ├── unigram+freq_fairness_lr.pck │ ├── unigram+freq_loyalty_lr.pck │ ├── unigram+freq_non-moral_lr.pck │ ├── unigram+freq_purity_lr.pck │ ├── unigram_authority_lr.pck │ ├── unigram_care_lr.pck │ ├── unigram_fairness_lr.pck │ ├── unigram_loyalty_lr.pck │ ├── unigram_non-moral_lr.pck │ └── unigram_purity_lr.pck ├── lexicon_use.py ├── lines.txt ├── moral_list.py ├── moralstrength.py └── moralstrengthdict.py ├── moralstrength_annotations ├── authority.tsv ├── care.tsv ├── fairness.tsv ├── loyalty.tsv └── purity.tsv ├── moralstrength_raw ├── RAW_ANNOTATIONS │ ├── Readme.txt │ ├── all_annotators_except_failed │ │ ├── RAW_ANNOTATIONS_AUTHORITY.txt │ │ ├── RAW_ANNOTATIONS_CARE.txt │ │ ├── RAW_ANNOTATIONS_FAIRNESS.txt │ │ ├── RAW_ANNOTATIONS_LOYALTY.txt │ │ └── RAW_ANNOTATIONS_PURITY.txt │ └── filtered_annotators │ │ ├── RAW_ANNOTATIONS_AUTHORITY.txt │ │ ├── RAW_ANNOTATIONS_CARE.txt │ │ ├── RAW_ANNOTATIONS_FAIRNESS.txt │ │ ├── RAW_ANNOTATIONS_LOYALTY.txt │ │ └── RAW_ANNOTATIONS_PURITY.txt └── tasks │ ├── Editor Preview of Task — Authority.pdf │ ├── Editor Preview of Task — Care.pdf │ ├── Editor Preview of Task — Fairness.pdf │ ├── Editor Preview of Task — Loyalty.pdf │ └── Editor Preview of Task — Purity.pdf ├── requirements.txt ├── setup.cfg ├── setup.py └── tests ├── __init__.py ├── lexicon_use_test.py ├── moralstrength_test.py └── test_examples.txt /.gitignore: -------------------------------------------------------------------------------- 1 | # 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If the Library as you 157 | received it does not specify a version number of the GNU Lesser 158 | General Public License, you may choose any version of the GNU Lesser 159 | General Public License ever published by the Free Software Foundation. 160 | 161 | If the Library as you received it specifies that a proxy can decide 162 | whether future versions of the GNU Lesser General Public License shall 163 | apply, that proxy's public statement of acceptance of any version is 164 | permanent authorization for you to choose that version for the 165 | Library. 166 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include requirements.txt 2 | include moralstrength/export/* 3 | include moralstrength/annotations/* 4 | include moralstrength/annotations/v1.1/* 5 | -------------------------------------------------------------------------------- /Predictor.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import numpy as np\n", 11 | "from sklearn.feature_extraction.text import CountVectorizer\n", 12 | "from sklearn.linear_model import LogisticRegression\n", 13 | "from sklearn.pipeline import Pipeline\n", 14 | "\n", 15 | "from lexicon_use import form_text_vector\n", 16 | "from estimators import estimate\n", 17 | "\n", 18 | "from gsitk.preprocess import pprocess_twitter, simple, Preprocessor\n", 19 | "\n", 20 | "\n", 21 | "moral_options = ('care', 'fairness', 'loyalty', 'authority', 'purity', 'non-moral')" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "Predict a single text.\n", 29 | "In the following result, the output is the estimated probability of being relevant to either a vice or virtue of the corresponding moral trait." 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 2, 35 | "metadata": { 36 | "scrolled": true 37 | }, 38 | "outputs": [ 39 | { 40 | "data": { 41 | "text/plain": [ 42 | "array([0.99926174])" 43 | ] 44 | }, 45 | "execution_count": 2, 46 | "metadata": {}, 47 | "output_type": "execute_result" 48 | } 49 | ], 50 | "source": [ 51 | "# example from real tweet\n", 52 | "text = '''\n", 53 | "PLS help #HASHTAG's family. No one prepares for this. They are in need of any assistance you can offer\n", 54 | "'''\n", 55 | "\n", 56 | "estimate(text, moral='care', model='unigram+count+freq')" 57 | ] 58 | }, 59 | { 60 | "cell_type": "markdown", 61 | "metadata": {}, 62 | "source": [ 63 | "Any combination of the following models is valid:\n", 64 | "* count\n", 65 | "* freq\n", 66 | "* unigram\n", 67 | "* simon\n", 68 | "\n", 69 | "For example, it is possible to use `simon+unigram`, `simon+unigram+count`, etc. Order does not affect the chosen model." 70 | ] 71 | }, 72 | { 73 | "cell_type": "markdown", 74 | "metadata": {}, 75 | "source": [ 76 | "Also, it is possible to predict from a file, `lines.txt`. The format of this file is a text to analyze per line." 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": 3, 82 | "metadata": {}, 83 | "outputs": [ 84 | { 85 | "name": "stdout", 86 | "output_type": "stream", 87 | "text": [ 88 | "My cat is happy\r\n", 89 | "I really care for my cat\r\n", 90 | "She hates going to the movies\r\n", 91 | "PLS help #HASHTAG's family. No one prepares for this. They are in need of any assistance you can offer\r\n" 92 | ] 93 | } 94 | ], 95 | "source": [ 96 | "!cat lines.txt" 97 | ] 98 | }, 99 | { 100 | "cell_type": "code", 101 | "execution_count": 4, 102 | "metadata": { 103 | "scrolled": true 104 | }, 105 | "outputs": [ 106 | { 107 | "data": { 108 | "text/plain": [ 109 | "array([0.42219461, 0.82648571, 0.07591536, 0.99926174])" 110 | ] 111 | }, 112 | "execution_count": 4, 113 | "metadata": {}, 114 | "output_type": "execute_result" 115 | } 116 | ], 117 | "source": [ 118 | "estimate(None, moral='care', model='unigram+count+freq', from_file=True)" 119 | ] 120 | } 121 | ], 122 | "metadata": { 123 | "kernelspec": { 124 | "display_name": "Python 3", 125 | "language": "python", 126 | "name": "python3" 127 | }, 128 | "language_info": { 129 | "codemirror_mode": { 130 | "name": "ipython", 131 | "version": 3 132 | }, 133 | "file_extension": ".py", 134 | "mimetype": "text/x-python", 135 | "name": "python", 136 | "nbconvert_exporter": "python", 137 | "pygments_lexer": "ipython3", 138 | "version": "3.5.2" 139 | } 140 | }, 141 | "nbformat": 4, 142 | "nbformat_minor": 2 143 | } 144 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Moral Foundations Theory predictor and lexicon 2 | 3 | 4 | **Table of Contents** 5 | 6 | - [Moral Foundations Theory predictor and lexicon](#moral-foundations-theory-predictor-and-lexicon) 7 | - [NEW! Liberty lexicon 2nd version - LibertyMFD](#new-liberty-lexicon-2nd-version---libertymfd) 8 | - [Liberty lexicon 1st version](#liberty-lexicon-1st-version) 9 | - [Install](#install) 10 | - [GUI](#gui) 11 | - [MoralStrength lexicon](#moralstrength-lexicon) 12 | - [MoralStrength processed lexicon](#moralstrength-processed-lexicon) 13 | - [MoralStrength presence](#moralstrength-presence) 14 | - [Unsupervised prediction text using MoralStrength](#unsupervised-prediction-text-using-moralstrength) 15 | - [Changing lexicon version](#changing-lexicon-version) 16 | - [List of methods to use](#list-of-methods-to-use) 17 | - [MoralStrength raw lexicon](#moralstrength-raw-lexicon) 18 | - [MoralStrength annotation task descriptions](#moralstrength-annotation-task-descriptions) 19 | 20 | 21 | 22 | 23 | This repository contains code and trained models corresponding to the paper "MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction". 24 | Run `Predictor.ipynb` to see a functioning version of the moral foundations predictor. Keep reading for some examples of use below. 25 | 26 | ## NEW! Liberty lexicon 2nd version - LibertyMFD 27 | 28 | On an new work, we have generated two new versions of the *Liberty/oppression* moral foundation lexicon: the _LibertyMFD_ lexicon. 29 | The lexicons are accessible in this repository, in the `liberty/2nd_version` folder ([link here](https://github.com/oaraque/moral-foundations/tree/master/liberty/2nd_version)). 30 | We expect to update this lexicon soon. 31 | 32 | If you use this lexicon, please cite the [following publication](https://doi.org/10.1145/3524458.3547264): 33 | ``` 34 | @inproceedings{10.1145/3524458.3547264, 35 | author = {Araque, Oscar and Gatti, Lorenzo and Kalimeri, Kyriaki}, 36 | title = {LibertyMFD: A Lexicon to Assess the Moral Foundation of Liberty.}, 37 | year = {2022}, 38 | isbn = {9781450392846}, 39 | publisher = {Association for Computing Machinery}, 40 | address = {New York, NY, USA}, 41 | url = {https://doi.org/10.1145/3524458.3547264}, 42 | doi = {10.1145/3524458.3547264}, 43 | booktitle = {Proceedings of the 2022 ACM Conference on Information Technology for Social Good}, 44 | pages = {154–160}, 45 | numpages = {7}, 46 | keywords = {lexicon, natural language processing, word embeddings, liberty, moral foundations theory, moral values}, 47 | location = {Limassol, Cyprus}, 48 | series = {GoodIT '22} 49 | } 50 | ``` 51 | 52 | ## Liberty lexicon 1st version 53 | 54 | We have generated a new lexicon that contains the *Liberty/oppression* moral foundation. 55 | To access the lexicon, see the `liberty/1st_version` folder ([link here](https://github.com/oaraque/moral-foundations/tree/master/liberty/1st_version)). 56 | This lexicon will be updated regularly. 57 | 58 | If you use the **liberty lexicon**, please cite the following paper: 59 | ``` 60 | DOI: https://doi.org/10.1145/3442442.3452351 61 | ``` 62 | 63 | ## Install 64 | 65 | The software is written in Python 3. For installing, please use `pip`: 66 | 67 | ``` 68 | pip install moralstrength 69 | ``` 70 | 71 | ## GUI 72 | 73 | This repository is intended for users that are willing to use the software through Python. 74 | Alternatively, we have published a Graphical Interface that works on Linux, MacOS, and Windows. Please visit [this repository](https://github.com/oaraque/moral-foundations-gui). 75 | 76 | # MoralStrength lexicon 77 | 78 | ## MoralStrength processed lexicon 79 | 80 | This repository contains the MoralStrength lexicon, which enables researchers to extract the moral valence from a variety of lemmas. 81 | It is available under the directory `moralstrength_annotations`. 82 | An example of use of the lexicon with Python is: 83 | 84 | ```python 85 | >>> import moralstrength 86 | 87 | >>> moralstrength.word_moral_annotations('care') 88 | {'care': 8.8, 'fairness': -1, 'loyalty': -1, 'authority': -1, 'purity': -1} 89 | ``` 90 | 91 | ## MoralStrength presence 92 | 93 | Also, this repository contains several already-trained models that predict the presence of a certain moral trait. 94 | That is, whether the analyzed text is relevant for a moral trait, or not. 95 | A minimal example of use: 96 | 97 | ```python 98 | import moralstregnth 99 | 100 | text = "PLS help #HASHTAG's family. No one prepares for this. They are in need of any assistance you can offer" 101 | 102 | moralstrength.string_moral_value(text, moral='care') 103 | ``` 104 | 105 | You can check the available moral traits using the `moralstrength.lexicon_morals` method. 106 | The complete list of methods that can be used is shown in the next section. 107 | 108 | ## Unsupervised prediction text using MoralStrength 109 | 110 | This package offers a function to perform unsupervised prediction over a list of texts, giving the prediction in a organized fashion. 111 | For example: 112 | 113 | ```python 114 | from moralstrength.moralstrength import estimate_morals 115 | 116 | texts = '''My dog is very loyal to me. 117 | My cat is not loyal, but understands my authority. 118 | He did not want to break the router, he was fixing it. 119 | It is not fair! She cheated on the exams. 120 | Are you pure of heart? Because I am sure not. 121 | Will you take care of me? I am sad.''' 122 | 123 | texts = texts.split('\n') 124 | 125 | result = estimate_morals(texts, process=True) # set to false if text is alredy pre-processed 126 | print(result) 127 | ``` 128 | 129 | The result of this short script would be as follows. 130 | The estimation is given in a [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) format. 131 | ``` 132 | care fairness loyalty authority purity 133 | 0 NaN NaN 8.875 5.1250 NaN 134 | 1 NaN NaN 8.875 6.9625 NaN 135 | 2 NaN NaN NaN NaN NaN 136 | 3 NaN 9.0 NaN NaN NaN 137 | 4 NaN NaN NaN NaN 9.0 138 | 5 8.8 NaN NaN NaN NaN 139 | 140 | ``` 141 | 142 | ## Changing lexicon version 143 | 144 | The original version of the MoralStrength lexicon is described here: 145 | 146 | ``` 147 | Oscar Araque, Lorenzo Gatti, Kyriaki Kalimeri, 148 | MoralStrength: Exploiting a moral lexicon and embedding similarity for moral foundations prediction, 149 | Knowledge-Based Systems, 150 | Volume 191, 151 | 2020, 152 | 105184, 153 | ISSN 0950-7051, 154 | https://doi.org/10.1016/j.knosys.2019.105184. 155 | (http://www.sciencedirect.com/science/article/pii/S095070511930526X) 156 | ``` 157 | 158 | which is also open in arXiv [https://arxiv.org/abs/1904.08314](https://arxiv.org/abs/1904.08314). 159 | 160 | A new improved version of the lexicon can be used to predict moral values. 161 | By default, the software uses the last version. 162 | to use the original version, you can do: 163 | 164 | ```python 165 | from moralstrength import lexicon_use 166 | 167 | lexicon_use.select_version("original") 168 | # predict here moral values using the original MoralStrength 169 | ``` 170 | 171 | If at any moment you want to use the new version of the lexicon again, just do: 172 | 173 | ```python 174 | lexicon_use.select_version("latest") 175 | ``` 176 | 177 | 178 | ## List of methods to use 179 | 180 | The methods that are under `moralstrength.moralstrength` are the following: 181 | ``` 182 | get_available_lexicon_traits() 183 | Returns a list of traits that were annotated and can be queried 184 | by word_moral_value(). 185 | care: Care/Harm 186 | fairness: Fairness/Cheating 187 | loyalty: Loyalty/Betrayal 188 | authority: Authority/Subversion 189 | purity: Purity/Degradation 190 | 191 | get_available_models() 192 | Returns a list of available models for predicting texts. 193 | Short explanation of names: 194 | unigram: simple unigram-based model 195 | count: number of words that are rated as closer to moral extremes 196 | freq: distribution of moral ratings across the text 197 | simon: SIMilarity-based sentiment projectiON 198 | or a combination of these. 199 | For a comprehensive explanation of what each model does and how it performs on 200 | different datasets, see https://arxiv.org/abs/1904.08314 201 | (published at Knowledge-Based Systems). 202 | 203 | get_available_prediction_traits() 204 | Returns a list of traits that can be predicted by string_moral_value() 205 | or file_moral_value(). 206 | care: Care/Harm 207 | fairness: Fairness/Cheating 208 | loyalty: Loyalty/Betrayal 209 | authority: Authority/Subversion 210 | purity: Purity/Degradation 211 | non-moral: Tweet/text is non-moral 212 | 213 | string_average_moral(text, moral) 214 | Returns the average of the annotations for the words in the sentence (for one moral). 215 | If no word is recognized/found in the lexicon, returns -1. 216 | Words are lemmatized using spacy. 217 | 218 | string_moral_value(text, moral, model='unigram+freq') 219 | Returns the estimated probability that the text is relevant to either a vice or 220 | virtue of the corresponding moral trait. 221 | The default model is unigram+freq, the best performing (on average) across all 222 | dataset, according to our work. 223 | For a list of available models, see get_available_models(). 224 | For a list of traits, get_available_prediction_traits(). 225 | 226 | string_moral_values(text, model='unigram+freq') 227 | Returns the estimated probability that the text is relevant to vices or virtues 228 | of all moral traits, as a dict. 229 | The default model is unigram+freq, the best performing (on average) across all 230 | dataset, according to our work. 231 | For a list of available models, see get_available_models(). 232 | For a list of traits, get_available_prediction_traits(). 233 | 234 | word_moral_value(word, moral) 235 | Returns the association strength between word and moral trait, 236 | as rated by annotators. Value ranges from 1 to 9. 237 | 1: words closely associated to harm, cheating, betrayal, subversion, degradation 238 | 9: words closely associated to care, fairness, loyalty, authority, sanctity 239 | If the word is not in the lexicon of that moral trait, returns -1. 240 | For a list of available traits, get_available_lexicon_traits() 241 | 242 | word_moral_values(word) 243 | Returns a dict that gives the association strength between word and every 244 | moral trait, as rated by annotators. Value ranges from 1 to 9. 245 | 1: words closely associated to harm, cheating, betrayal, subversion, degradation 246 | 9: words closely associated to care, fairness, loyalty, authority, purity/sanctity 247 | If the word is not in the lexicon of that moral trait, returns -1. 248 | ``` 249 | ## MoralStrength raw lexicon 250 | 251 | The `moralstrength_raw` folder contains the raw annotations collected from figure-eight. 252 | The folder all_annotators_except_failed contains all the annotations collected, except for the annotators that failed the task (see the paper for details on the control questions, which were based on valence ratings from Warriner et al.). 253 | The folder filtered_annotators contains the annotations after the annotators with low inter-annotator agreement were removed. 254 | 255 | The filename is `RAW_ANNOTATIONS_[MORAL]`, where MORAL is the moral trait considered and can either be AUTHORITY, CARE, FAIRNESS, LOYALTY or PURITY. 256 | 257 | The fields in each file are: 258 | - WORD the word to be annotated 259 | - ANNOTATOR_ID the unique ID of each annotator 260 | - VALENCE the valence rating of WORD, on a scale from 1 (low) to 9 (high) 261 | - AROUSAL the arousal rating of WORD, on a scale from 1 (low) to 9 (high) 262 | - RELEVANCE whether WORD is related to the MORAL 263 | - EXPRESSED_MORAL the moral strength of WORD, i.e. whether it is closer to one or the other extremes pertaining the MORAL trait. 264 | 265 | The numbers for EXPRESSED_MORAL range from 1 to 9, and the extremes of the scales are: 266 | - 1=Subversion, 9=Authority for AUTHORITY 267 | - 1=Harm, 9=Care for CARE 268 | - 1=Proportionality, 9=Fairness for FAIRNESS 269 | - 1=Disloyalty, 9=Loyalty for LOYALTY 270 | - 1=Degradation, 9=Purity for PURITY 271 | 272 | For privacy reason, the annotator ID has been salted and hashed, so that going back to the original annotator ID is not possible, but it is still possible to track each annotator's ratings across the different morals. 273 | 274 | ## MoralStrength annotation task descriptions 275 | 276 | In the folder `moralstrength/tasks` we also include the original description of the annotation tasks for the crowdsourcing process. 277 | The interested reader can consult the instructions given to the human annotators. 278 | 279 | 280 | -------------------------------------------------------------------------------- /moralstrength/__init__.py: -------------------------------------------------------------------------------- 1 | from moralstrength.moralstrength import * 2 | -------------------------------------------------------------------------------- /moralstrength/annotations/authority.tsv: -------------------------------------------------------------------------------- 1 | LEMMA EXPRESSED_MORAL 2 | abide 4.16666666667 3 | agitation 2.8 4 | alienate 4.28571428571 5 | apostasy 4.0 6 | apostate 4.16666666667 7 | authoritarian 8.2 8 | authoritative 7.833333333330001 9 | authority 8.8 10 | betrayal 3.14285714286 11 | bourgeoisie 6.57142857143 12 | caste 6.75 13 | casteless 4.5 14 | class 6.875 15 | command 7.285714285710001 16 | compliance 6.8 17 | compliant 6.0 18 | comply 6.833333333330001 19 | control 8.0 20 | defect 4.33333333333 21 | defector 2.2 22 | defer 4.57142857143 23 | deferential 4.42857142857 24 | defiance 2.83333333333 25 | defiant 3.5 26 | defy 2.83333333333 27 | denounce 4.0 28 | deserted 3.8 29 | disloyal 4.55555555556 30 | disloyalty 2.2857142857099997 31 | disobedience 3.5 32 | disobey 3.16666666667 33 | disrespect 3.66666666667 34 | dissension 2.4 35 | dissent 3.83333333333 36 | dissentient 4.0 37 | dissident 2.0 38 | dutiable 6.4 39 | duty 7.166666666669999 40 | father 7.666666666669999 41 | fatherhood 6.0 42 | fatherless 4.66666666667 43 | fatherlike 4.6 44 | fatherliness 5.833333333330001 45 | heretic 4.5 46 | hierarchical 7.714285714289999 47 | hierarchy 6.42857142857 48 | honor 6.0 49 | honorarium 5.25 50 | honorary 7.2 51 | honoree 6.333333333330001 52 | honorific 6.25 53 | illegal 3.2 54 | illegalise 5.1428571428600005 55 | insubordination 5.16666666667 56 | insurgent 2.55555555556 57 | law 8.666666666669999 58 | lawless 2.16666666667 59 | leader 7.1428571428600005 60 | leadership 8.8 61 | legal 7.8 62 | legalisation 7.166666666669999 63 | loyal 5.125 64 | loyalist 7.8 65 | loyalty 5.4 66 | mother 6.5 67 | motherhood 6.333333333330001 68 | motherland 6.333333333330001 69 | motherless 3.66666666667 70 | motherlike 5.375 71 | motherliness 5.833333333330001 72 | mutinous 2.625 73 | nonconformist 3.4 74 | obedience 5.28571428571 75 | obey 4.4 76 | obstruct 4.0 77 | oppose 2.5 78 | order 8.0 79 | orderliness 7.0 80 | permission 6.5 81 | permit 6.166666666669999 82 | position 7.6 83 | preserve 5.0 84 | protest 2.8 85 | rank 7.2 86 | ranker 5.0 87 | rankin 5.0 88 | rebel 3.0 89 | rebellion 3.16666666667 90 | rebellious 2.7142857142900003 91 | refuse 3.85714285714 92 | remonstrate 5.0 93 | respect 5.8 94 | revere 7.0 95 | reverential 5.57142857143 96 | riot 2.3 97 | rioter 2.1428571428599996 98 | riotous 3.0 99 | sedition 3.0 100 | serve 4.8 101 | status 5.16666666667 102 | submission 6.0 103 | submit 4.75 104 | submitter 4.375 105 | subversion 1.2 106 | subvert 3.0 107 | supremacy 6.833333333330001 108 | traditional 5.8 109 | traditionalist 5.88888888889 110 | traitor 1.75 111 | treacherous 3.6 112 | unfaithful 2.0 113 | veneration 6.666666666669999 114 | obedience 7.0 115 | offence 3.8 116 | forbid 4.16666666666667 117 | garrison 4.75 118 | mildly 4.8 119 | mandate 7.142857142857139 120 | rigidly 4.6 121 | exemplary 6.4 122 | perilous 4.5 123 | anxiety 6.0 124 | patience 5.75 125 | polite 7.6 126 | belief 5.0 127 | cherish 3.6 128 | devoted 4.2 129 | indifference 3.0 130 | revolt 2.0 131 | classify 6.0 132 | -------------------------------------------------------------------------------- /moralstrength/annotations/care.tsv: -------------------------------------------------------------------------------- 1 | LEMMA EXPRESSED_MORAL 2 | abandon 1.6 3 | abuse 2.5 4 | amity 5.42857142857 5 | annihilation 1.33333333333 6 | attack 2.66666666667 7 | benefit 7.0 8 | brutal 2.33333333333 9 | brutalise 1.2 10 | care 8.8 11 | compassion 7.0 12 | compassionate 8.166666666669999 13 | cruelly 1.6 14 | cruelty 3.4 15 | crush 2.4 16 | crusher 2.8 17 | damage 3.0 18 | defendant 3.2 19 | defenestration 3.83333333333 20 | defense 5.0 21 | defenseless 4.4 22 | destroy 1.0 23 | detrimental 2.4 24 | empathetic 6.8 25 | empathy 8.5 26 | endangered 4.6 27 | endangerment 1.8 28 | exploit 2.83333333333 29 | fight 4.0 30 | guard 6.8 31 | hurt 2.2857142857099997 32 | impair 2.6 33 | kill 1.6 34 | killer 1.0 35 | peace 7.8 36 | peaceable 7.8571428571399995 37 | peacekeeping 7.8 38 | peacemaker 8.6 39 | peacenik 6.4 40 | peacetime 7.8 41 | preserve 7.6 42 | protection 8.0 43 | protectionism 6.8 44 | protectionist 8.2 45 | protector 8.57142857143 46 | protectorship 8.8 47 | ravage 3.4 48 | ruin 1.4 49 | safe 8.6 50 | safeguard 6.0 51 | safehold 6.6 52 | safekeeping 8.8 53 | safety 8.2 54 | security 8.2 55 | shelter 7.0 56 | shield 7.5 57 | spurn 3.0 58 | stomp 2.33333333333 59 | suffer 2.6 60 | sympathetic 8.333333333330001 61 | sympathize 8.0 62 | sympathy 8.57142857143 63 | violence 1.6666666666699999 64 | violent 2.83333333333 65 | war 1.6666666666699999 66 | wound 2.2 67 | disgusted 2.8 68 | abuse 1.7142857142857097 69 | quarrel 1.8333333333333302 70 | healthcare 8.6 71 | opponent 3.2 72 | refuge 6.75 73 | dignity 7.0 74 | assassin 1.0 75 | collateral 3.8333333333333304 76 | destruction 1.14285714285714 77 | bang 1.0 78 | preserving 7.4 79 | annihilation 1.16666666666667 80 | broken 2.1666666666666696 81 | intrusive 2.6 82 | wrecked 1.75 83 | critically 3.2 84 | upsetting 3.8333333333333304 85 | -------------------------------------------------------------------------------- /moralstrength/annotations/fairness.tsv: -------------------------------------------------------------------------------- 1 | LEMMA EXPRESSED_MORAL 2 | balance 5.6 3 | bias 5.2 4 | bigot 3.6 5 | bigotry 4.0 6 | constant 4.5 7 | discrimination 4.0 8 | discriminatory 4.0 9 | dishonest 5.2 10 | disproportion 3.16666666667 11 | disproportionate 3.6 12 | dissociate 3.83333333333 13 | egalitarian 6.0 14 | equable 6.2 15 | equal 7.0 16 | equaliser 6.2 17 | equalitarian 5.33333333333 18 | equity 5.125 19 | equivalent 5.0 20 | evenness 5.16666666667 21 | exclude 4.2 22 | exclusion 3.6 23 | fair 9.0 24 | fair-minded 7.166666666669999 25 | fair-mindedness 8.71428571429 26 | homologous 2.5 27 | honest 7.2 28 | honesty 7.666666666669999 29 | impartial 8.0 30 | injustice 6.5 31 | justice 7.6 32 | justification 6.1428571428600005 33 | justificatory 5.5 34 | justify 5.71428571429 35 | preference 5.4 36 | prejudgment 4.125 37 | prejudice 3.83333333333 38 | prejudicial 6.0 39 | reasonable 5.0 40 | reciprocal 6.4 41 | right 8.166666666669999 42 | segregation 5.75 43 | segregationism 2.2 44 | segregationist 2.8 45 | tolerant 7.166666666669999 46 | unbiased 8.6 47 | unequal 4.5 48 | unequalised 4.0 49 | unequalled 3.66666666667 50 | unfair 4.625 51 | unjust 6.333333333330001 52 | unjustified 4.625 53 | unprejudiced 6.71428571429 54 | unscrupulous 2.7142857142900003 55 | slavery 3.6666666666666696 56 | impartial 7.0 57 | permanency 5.16666666666667 58 | wrongfully 5.4 59 | homophobic 4.0 60 | inhumane 4.66666666666667 61 | bigotry 4.33333333333333 62 | inaccurate 4.4 63 | subjective 5.5 64 | competent 6.4 65 | consumerism 5.5 66 | amplitude 5.16666666666667 67 | honest 7.55555555555556 68 | disproportionately 3.28571428571429 69 | accepting 7.83333333333333 70 | relatively 4.875 71 | oppression 5.75 72 | proportionality 3.0 73 | -------------------------------------------------------------------------------- /moralstrength/annotations/loyalty.tsv: -------------------------------------------------------------------------------- 1 | LEMMA EXPRESSED_MORAL 2 | abandon 1.42857142857 3 | ally 8.71428571429 4 | apostasy 3.8 5 | apostate 4.2 6 | betrayal 1.0 7 | cadre 6.0 8 | clique 5.88888888889 9 | cliquish 5.4 10 | cohort 6.0 11 | collective 7.333333333330001 12 | collectivism 7.1111111111100005 13 | collectivist 6.4 14 | communal 6.2 15 | commune 5.375 16 | communism 4.42857142857 17 | communist 3.85714285714 18 | community 6.2 19 | comradeship 7.57142857143 20 | deceit 1.125 21 | deceive 1.33333333333 22 | deserted 2.77777777778 23 | devoted 8.0 24 | devotedness 8.0 25 | devotee 7.2 26 | disloyal 1.8571428571400002 27 | disloyalty 1.8333333333300001 28 | family 8.0 29 | fellow 7.1428571428600005 30 | fellowship 8.166666666669999 31 | foreign 4.5 32 | group 6.375 33 | guild 7.1428571428600005 34 | homeland 7.1428571428600005 35 | immigration 4.83333333333 36 | imposter 2.2857142857099997 37 | individual 4.28571428571 38 | individualised 4.0 39 | individualistic 3.8 40 | insider 5.625 41 | jilted 2.375 42 | joint 6.833333333330001 43 | loyal 8.875 44 | loyalist 7.555555555560001 45 | loyalty 7.555555555560001 46 | member 6.625 47 | miscreant 2.8 48 | national 5.2 49 | nationalisation 5.6 50 | nationalist 6.0 51 | nationhood 7.0 52 | nationwide 6.0 53 | patriot 7.8571428571399995 54 | renegade 4.16666666667 55 | segregation 4.75 56 | segregationism 3.14285714286 57 | segregationist 4.5 58 | sequester 4.71428571429 59 | solidarity 8.6 60 | spy 2.875 61 | terrorise 1.6 62 | terrorism 3.85714285714 63 | terrorist 2.66666666667 64 | together 7.2 65 | traitor 1.8 66 | treacherous 1.57142857143 67 | unison 7.5 68 | united 8.5 69 | allegiance 8.9 70 | dedication 6.4 71 | founder 6.375 72 | lust 4.42857142857143 73 | siding 7.5 74 | amity 7.857142857142861 75 | cowardice 3.25 76 | loyalist 8.571428571428571 77 | bandit 1.5 78 | nationalism 6.83333333333333 79 | consulate 6.33333333333333 80 | rogue 2.6666666666666696 81 | insulting 3.5 82 | cruelty 3.3333333333333304 83 | clap 7.5 84 | lovingly 7.0 85 | -------------------------------------------------------------------------------- /moralstrength/annotations/purity.tsv: -------------------------------------------------------------------------------- 1 | LEMMA EXPRESSED_MORAL 2 | abstinence 7.714285714289999 3 | adulterated 2.83333333333 4 | adulterine 4.11111111111 5 | adultery 1.5 6 | apostasy 3.2 7 | apostate 3.5 8 | austerity 5.666666666669999 9 | blemish 4.0 10 | celibacy 8.0 11 | celibate 7.625 12 | chaste 7.57142857143 13 | chastening 6.5 14 | church 6.833333333330001 15 | churchgoer 7.4 16 | churchgoing 5.42857142857 17 | churchman 7.0 18 | clean 7.666666666669999 19 | cleaner 7.5 20 | cleanliness 7.6 21 | cleansing 8.0 22 | cleanup 6.5 23 | contagion 2.7142857142900003 24 | contagious 3.5 25 | debauchee 2.6 26 | debauchery 2.2 27 | decent 8.0 28 | decentalisation 5.0 29 | depraved 3.25 30 | desecration 2.75 31 | dirt 2.375 32 | dirtily 3.22222222222 33 | dirty 3.0 34 | disease 3.0 35 | disgustingness 2.16666666667 36 | exploit 3.16666666667 37 | exploitatory 2.7142857142900003 38 | filth 2.2 39 | filthily 5.0 40 | filthy 2.0 41 | gross 2.625 42 | heretic 2.5714285714300003 43 | holy 8.5 44 | impiety 3.5714285714300003 45 | impious 3.2 46 | indecent 2.8 47 | innocent 8.4 48 | integrity 7.8 49 | intemperate 4.28571428571 50 | lax 2.66666666667 51 | limpid 4.5 52 | maiden 7.4 53 | modesty 8.166666666669999 54 | obscene 1.4 55 | pervert 3.16666666667 56 | piety 7.42857142857 57 | pious 7.0 58 | preserve 6.833333333330001 59 | pristine 7.0 60 | profanatory 4.0 61 | profanity 2.6 62 | profligate 2.85714285714 63 | promiscuous 3.14285714286 64 | prostitution 3.2857142857099997 65 | pure 9.0 66 | pureblood 7.0 67 | purebred 6.5 68 | purity 7.1111111111100005 69 | refined 6.8571428571399995 70 | repulsive 2.4285714285699997 71 | ruin 2.75 72 | sacred 8.5 73 | sacredness 8.4 74 | saint 8.57142857143 75 | sainthood 8.0 76 | saintlike 8.333333333330001 77 | saintliness 8.14285714286 78 | sick 2.7142857142900003 79 | sickening 2.25 80 | sin 2.0 81 | sinner 2.4285714285699997 82 | slut 2.75 83 | sluttish 1.33333333333 84 | stainable 5.16666666667 85 | stained 2.6 86 | stainer 4.1428571428600005 87 | stainless 6.166666666669999 88 | sterile 7.8 89 | sterilisation 4.375 90 | tainted 2.0 91 | tarnish 3.66666666667 92 | tramp 3.0 93 | trashy 1.33333333333 94 | unadulterated 6.0 95 | unchaste 2.66666666667 96 | uncleanliness 2.76923076923 97 | upright 6.0 98 | virgin 7.555555555560001 99 | virtuous 7.166666666669999 100 | wanton 2.6 101 | wholesome 8.4 102 | whore 1.57142857143 103 | wicked 1.8571428571400002 104 | wretched 1.875 105 | wretchedness 3.0 106 | protect 7.75 107 | lush 3.4 108 | vertigo 4.8 109 | freshener 7.83333333333333 110 | deluded 3.0 111 | priest 7.2 112 | tasteless 5.33333333333333 113 | saint 7.0 114 | scam 2.3333333333333304 115 | patina 3.3333333333333304 116 | abstinence 4.5 117 | sting 3.0 118 | hygiene 5.857142857142861 119 | prostitution 1.6666666666666698 120 | perversion 1.7142857142857097 121 | Catholics 6.0 122 | -------------------------------------------------------------------------------- /moralstrength/annotations/v1.1/authority.tsv: -------------------------------------------------------------------------------- 1 | WORD EXPRESSED_MORAL 2 | -sama 6.25 3 | -san 6.25 4 | abandoned 3.8 5 | abide 4.16666666667 6 | abideby 4.16666666667 7 | abiding 4.16666666667 8 | accept 3.85714285714 9 | acrimony 2.4 10 | admirable 6.4 11 | admonish 5.0 12 | adoration 6.666666666669999 13 | adulterous 2.0 14 | advalorem 6.4 15 | agitate 2.8 16 | agitated 2.8 17 | agitating 2.8 18 | agitation 2.8 19 | agitators 2.8 20 | alienate 4.28571428571 21 | alienating 4.28571428571 22 | allow 6.166666666669999 23 | aloofness 3.0 24 | anarchic 2.16666666667 25 | anarchical 2.16666666667 26 | animadvert 5.0 27 | annoy 4.28571428571 28 | antagonize 4.28571428571 29 | anti-conformist 3.4 30 | anti-hindi 2.8 31 | anti-insurgent 2.55555555556 32 | anti-junta 2.0 33 | anti-traditionalist 5.88888888889 34 | anxeity 6.0 35 | anxieties 6.0 36 | anxiety 6.0 37 | anxiousness 6.0 38 | apathy 3.0 39 | apostacy 4.16666666667 40 | apostasize 4.0 41 | apostasy 4.0 42 | apostate 4.16666666667 43 | apostates 4.0 44 | apostatized 4.0 45 | apostatizing 4.0 46 | arch-heretic 4.5 47 | arduous 4.5 48 | assenting 4.0 49 | atkinson 5.0 50 | authorative 7.833333333330001 51 | authoritarian 8.2 52 | authoritarianism 8.2 53 | authoritative 7.833333333330001 54 | authoritativeness 7.833333333330001 55 | authoritive 7.833333333330001 56 | authority 8.799999999999999 57 | authroity 8.8 58 | autocratic 8.2 59 | bacchanalian 3.0 60 | barracks 4.75 61 | beleif 5.0 62 | belief 5.0 63 | believing 5.0 64 | belligerent 3.5 65 | bennett 5.0 66 | betrayal 3.14285714286 67 | betrayed 3.14285714286 68 | betrayer 3.14285714286 69 | betrayers 1.75 70 | betraying 3.14285714286 71 | blasphemer 4.5 72 | boisterous 3.0 73 | bourgeois 6.57142857143 74 | bourgeoisie 6.57142857143 75 | brother 7.666666666669999 76 | brotherless 4.66666666667 77 | calamitous 4.5 78 | callousness 3.0 79 | calss 6.875 80 | caste 6.75 81 | caste-based 6.75 82 | caste-ridden 4.5 83 | caste-system 6.75 84 | casteism 6.75 85 | casteist 6.75 86 | casteless 4.5 87 | castes 4.5 88 | castigate 4.0 89 | catagorize 6.0 90 | categorise 6.0 91 | categorize 6.0 92 | cheated-on 2.0 93 | cherish 3.6 94 | cherished 3.6 95 | cherishes 3.6 96 | cherishing 3.6 97 | child-rearing 6.0 98 | childrearing 6.0 99 | circumvent 3.0 100 | class 6.875 101 | classes 6.875 102 | classification 6.0 103 | classifies 6.0 104 | classify 6.0 105 | classifying 6.0 106 | co-leader 7.1428571428600005 107 | co-opt 3.0 108 | co-recipients 6.333333333330001 109 | command 7.285714285710001 110 | commendable 6.4 111 | commnad 7.285714285710001 112 | complaince 6.8 113 | compliance 6.8 114 | compliance-related 6.8 115 | compliancethe 6.8 116 | compliancy 6.0 117 | compliant 6.0 118 | complied 6.833333333330001 119 | complies 6.833333333330001 120 | comply 6.833333333330001 121 | complying 6.833333333330001 122 | condemn 4.0 123 | conform 6.833333333330001 124 | conformant 6.0 125 | conformist 3.4 126 | conserve 5.0 127 | considerate 7.6 128 | contorl 8.0 129 | control 8.0 130 | controlled 8.0 131 | controlling 8.0 132 | conventicle 4.0 133 | conventional 5.8 134 | cormack 5.0 135 | counter-insurgent 2.55555555556 136 | counterinsurgent 2.55555555556 137 | court-martialed 5.16666666667 138 | court-martialled 5.16666666667 139 | courteous 7.6 140 | criticize 4.0 141 | cuckolded 2.0 142 | dad 7.666666666669999 143 | dangerous 3.6 144 | daughter 7.666666666669999 145 | daughters 6.5 146 | decidedly 4.8 147 | decriminalisation 7.166666666669999 148 | decriminalising 7.166666666669999 149 | decriminalization 7.166666666669999 150 | decry 4.0 151 | dedicate 4.2 152 | dedicated 4.2 153 | dedicates 4.2 154 | dedicating 4.2 155 | defect 4.33333333333 156 | defected 2.2 157 | defecting 2.2 158 | defection 2.2 159 | defective 4.33333333333 160 | defector 2.2 161 | defects 4.33333333333 162 | defer 4.57142857143 163 | deference 4.42857142857 164 | deferential 4.42857142857 165 | deferentially 4.42857142857 166 | defering 4.57142857143 167 | deferral 4.57142857143 168 | deferred 4.57142857143 169 | deferring 4.57142857143 170 | defiance 2.83333333333 171 | defiant 3.5 172 | defied 2.83333333333 173 | defies 2.83333333333 174 | definitive 7.833333333330001 175 | defy 2.83333333333 176 | defying 2.83333333333 177 | deification 6.666666666669999 178 | denounce 4.0 179 | denouncing 4.0 180 | deserted 3.8 181 | deserter 2.2 182 | desertion 2.28571428571 183 | desolate 3.8 184 | desolated 3.8 185 | despise 7.0 186 | despotic 8.2 187 | destabilize 3.0 188 | devious 3.6 189 | devoted 4.2 190 | devotedto 4.2 191 | devoting 4.2 192 | devotion 6.666666666669999 193 | dictatorial 8.2 194 | diehard 7.8 195 | diffident 4.42857142857 196 | directive 7.142857142857139 197 | disagreement 2.4 198 | disallow 4.16666666666667 199 | discontented 2.625 200 | discord 2.4 201 | disdainful 4.42857142857 202 | dishonor 6.0 203 | disinterest 3.0 204 | disloyal 4.55555555556 205 | disloyalty 2.28571428571 206 | disobedience 3.5 207 | disobedient 3.5 208 | disobey 3.16666666667 209 | disobeyed 3.16666666667 210 | disobeying 3.5 211 | disorderliness 7.0 212 | disrespect 3.66666666667 213 | disrespectful 3.66666666667 214 | disrespectfulness 3.66666666667 215 | disrespecting 3.66666666667 216 | dissension 2.4 217 | dissent 3.83333333333 218 | dissenter 3.83333333333 219 | dissenters 3.83333333333 220 | dissentient 4.0 221 | dissenting 3.83333333333 222 | dissention 3.83333333333 223 | dissentions 2.4 224 | dissidence 3.83333333333 225 | dissident 2.0 226 | disunity 2.4 227 | divisiveness 2.4 228 | dogmatical 4.0 229 | dogmatically 4.6 230 | dominance 6.833333333330001 231 | domination 6.833333333330001 232 | double-agent 2.2 233 | duplicitous 3.6 234 | dutiable 6.4 235 | duties 7.166666666669999 236 | duty 7.166666666669999 237 | duty-paid 6.4 238 | earth-mother 5.833333333330001 239 | emeritus 7.2 240 | endear 4.28571428571 241 | entreat 5.0 242 | epithet 6.25 243 | estrange 4.28571428571 244 | ever-loyal 5.125 245 | exceptional 6.4 246 | excisable 6.4 247 | excise-equivalent 6.4 248 | excommunicated 4.5 249 | excoriate 4.0 250 | exemplarily 6.4 251 | exemplary 6.4 252 | exemplified 6.4 253 | exemplify 6.4 254 | exemplifying 6.4 255 | exigible 6.4 256 | expostulate 5.0 257 | faith 5.0 258 | faithful 5.125 259 | faithless 2.0 260 | faithlessness 2.28571428571 261 | fascistic 8.2 262 | father 7.666666666669999 263 | fatherhood 6.0 264 | fathering 6.0 265 | fatherland 6.333333333330001 266 | fatherless 4.66666666667 267 | fatherlessness 6.0 268 | fatherlike 4.6 269 | fatherliness 5.833333333330001 270 | firmly-held 5.0 271 | fixedly 4.6 272 | flout 4.16666666667 273 | flouting 2.83333333333 274 | forbade 4.16666666666667 275 | forbid 4.16666666666667 276 | forbidding 4.16666666666667 277 | forego 4.57142857143 278 | forgo 4.57142857143 279 | forsaken 3.8 280 | fort 4.75 281 | fortifications 4.75 282 | fortress 4.75 283 | fraught 4.5 284 | fully-compliant 6.0 285 | garrison 4.75 286 | garrisoned 4.75 287 | garrisoning 4.75 288 | glynn 5.0 289 | grandfather 7.666666666669999 290 | grandmother 6.5 291 | guerrilla 2.55555555556 292 | guerrillas 2.55555555556 293 | half-deserted 3.8 294 | haphazardness 7.0 295 | harrowing 4.5 296 | hazardous 4.5 297 | headstrong 2.7142857142900003 298 | hegemony 6.833333333330001 299 | heirarchical 7.714285714289999 300 | heirarchy 6.42857142857 301 | heresies 4.5 302 | heresy 4.0 303 | heretic 4.5 304 | heretical 4.5 305 | heritics 4.5 306 | hierarchal 7.714285714289999 307 | hierarchic 7.714285714289999 308 | hierarchical 7.714285714289999 309 | hierarchically 7.714285714289999 310 | hierarchies 7.714285714289999 311 | hierarchy 6.42857142857 312 | hierarchy-based 7.714285714289999 313 | high-treason 3.0 314 | hinder 4.0 315 | home-land 6.333333333330001 316 | homeland 6.333333333330001 317 | honary 7.2 318 | honor 6.0 319 | honoraria 5.25 320 | honorarily 7.2 321 | honorarium 5.25 322 | honorary 7.2 323 | honored 6.0 324 | honoree 6.333333333330001 325 | honorific 6.25 326 | honoring 6.0 327 | honors 6.333333333330001 328 | honory 7.2 329 | honourees 6.333333333330001 330 | htsus 6.4 331 | idolize 7.0 332 | illegal 3.2 333 | illegalise 5.1428571428600005 334 | illegally 3.2 335 | immovably 4.6 336 | impede 4.0 337 | impeding 4.0 338 | impolite 7.6 339 | incitement 3.0 340 | incomprehension 3.0 341 | indifference 3.0 342 | indifferent 3.0 343 | indignantly 5.0 344 | indiscipline 5.16666666667 345 | inductee 6.333333333330001 346 | infidelity 2.0 347 | inflexibly 4.6 348 | infuriate 4.28571428571 349 | inhibit 4.0 350 | insolence 5.16666666667 351 | insolent 3.5 352 | insubordinate 4.55555555556 353 | insubordination 5.16666666667 354 | insurgencies 2.55555555556 355 | insurgency 2.55555555556 356 | insurgent 2.55555555556 357 | insurrection 3.16666666667 358 | 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398 | mandate 7.142857142857139 399 | mandating 7.142857142857139 400 | mannerly 7.6 401 | maternalism 5.833333333330001 402 | maternalistic 5.833333333330001 403 | mcmullen 5.0 404 | milburn 5.0 405 | mildly 4.8 406 | mini-riot 2.3 407 | minorly 4.8 408 | misdemeanour 3.8 409 | moderately 4.8 410 | mom 6.5 411 | momhood 6.333333333330001 412 | mommy-hood 6.333333333330001 413 | mommyhood 6.333333333330001 414 | more-than-slightly 4.8 415 | mother 6.5 416 | mother-country 6.333333333330001 417 | mother-less 4.66666666667 418 | mother-love 5.833333333330001 419 | motherhood 6.333333333330001 420 | mothering 6.333333333330001 421 | motherland 6.333333333330001 422 | motherless 3.66666666667 423 | motherlike 5.375 424 | motherliness 5.833333333330001 425 | motherlove 5.833333333330001 426 | motherly 5.833333333330001 427 | murtad 4.16666666667 428 | mutineer 2.625 429 | mutineers 2.625 430 | mutinied 2.625 431 | mutinous 2.625 432 | mutiny 5.16666666667 433 | mutinying 2.625 434 | near-deserted 3.8 435 | near-riot 2.3 436 | neatness 7.0 437 | nervousness 6.0 438 | non-authoritative 7.833333333330001 439 | non-belief 5.0 440 | non-compliance 6.8 441 | non-compliant 6.0 442 | non-conformism 3.4 443 | non-conformists 3.4 444 | non-legal 7.8 445 | non-traditional 5.8 446 | non-traditionalist 5.88888888889 447 | non-traditionalists 5.88888888889 448 | noncompliance 6.8 449 | nonconformist 3.4 450 | nonconformity 3.4 451 | nonlegal 7.8 452 | nose-thumbing 2.83333333333 453 | not-so-traditional 5.8 454 | obedi 5.28571428571 455 | obedi- 5.28571428571 456 | obedience 5.28571428571 457 | obedient 5.28571428571 458 | obeisant 4.42857142857 459 | obey 4.4 460 | obeyed 3.16666666667 461 | obeying 3.16666666667 462 | obligation 7.166666666669999 463 | obsequious 4.42857142857 464 | obsequiously 4.42857142857 465 | obstinance 2.83333333333 466 | obstruct 4.0 467 | obstructed 4.0 468 | obstructing 4.0 469 | obstruction 4.0 470 | offence 3.8 471 | offences 3.8 472 | offend 4.28571428571 473 | offenses 3.8 474 | oppose 2.5 475 | opposing 2.5 476 | opposition 2.5 477 | oppositionist 2.0 478 | order 8.0 479 | ordered 8.0 480 | ordering 8.0 481 | orderliness 7.0 482 | orderly 7.0 483 | orphan 4.66666666667 484 | orphaned 3.66666666667 485 | orphans 4.66666666667 486 | outcaste 4.5 487 | outrank 7.2 488 | paitence 5.75 489 | parent-less 4.66666666667 490 | parenthood 6.0 491 | parenting 6.0 492 | parentless 4.66666666667 493 | pateince 5.75 494 | patience 5.75 495 | patterson 5.0 496 | pennit 6.166666666669999 497 | perfidious 3.6 498 | perilous 4.5 499 | perilously 4.5 500 | permisison 6.5 501 | permission 6.5 502 | permit 6.166666666669999 503 | permitted 6.166666666669999 504 | permitting 6.166666666669999 505 | perserve 5.0 506 | perseverance 5.75 507 | petit-bourgeois 6.57142857143 508 | petty-bourgeois 6.57142857143 509 | petty-bourgeoisie 6.57142857143 510 | petulant 3.5 511 | polite 7.6 512 | posiiton 7.6 513 | position 7.6 514 | postpone 4.57142857143 515 | pre-eminence 6.833333333330001 516 | pre-nominal 6.25 517 | precarious 4.5 518 | preeminence 6.833333333330001 519 | premission 6.5 520 | preponderating 4.0 521 | preservation 5.0 522 | preserve 5.0 523 | primacy 6.833333333330001 524 | pro-democracy 2.0 525 | prodemocracy 2.0 526 | prohibit 4.16666666666667 527 | prohibiting 4.16666666666667 528 | proletarian 6.57142857143 529 | proletarians 6.57142857143 530 | proletariat 6.57142857143 531 | protest 2.7999999999999994 532 | protester 2.14285714286 533 | protesters 2.8 534 | protesting 2.8 535 | protestor 2.8 536 | protestors 2.8 537 | purist 5.88888888889 538 | purposefulness 7.0 539 | quasi-legal 7.8 540 | racous 3.0 541 | rank 6.466666666666666 542 | ranked 7.2 543 | ranker 5.0 544 | rankin 5.0 545 | ranking 7.2 546 | rankings 7.2 547 | raucous 3.0 548 | re-classify 6.0 549 | re-exportation 6.4 550 | re-ranking 5.0 551 | re-ranks 5.0 552 | re-submit 4.75 553 | rebel 3.0 554 | rebelious 3.0 555 | rebelling 3.0 556 | rebellion 3.16666666667 557 | rebellions 2.0 558 | rebellious 2.7142857142900003 559 | rebelliously 2.7142857142900003 560 | rebelliousness 2.83333333333 561 | reclassify 6.0 562 | refusal 3.85714285714 563 | refuse 3.85714285714 564 | refuseto 3.85714285714 565 | refusing 3.85714285714 566 | regimented 4.6 567 | reject 2.5 568 | reliquidation 6.4 569 | remonstrance 5.0 570 | remonstrate 5.0 571 | remonstrating 5.0 572 | remuneration 5.25 573 | renumeration 5.25 574 | repressive 8.2 575 | reprimand 5.16666666667 576 | repsect 5.8 577 | repudiate 4.0 578 | resolute 3.5 579 | respect 5.8 580 | respectful 7.6 581 | respecting 5.8 582 | resubmit 4.75 583 | reverance 5.57142857143 584 | reverant 5.57142857143 585 | revere 7.0 586 | reverence 7.0 587 | reverenced 6.666666666669999 588 | reverent 5.57142857143 589 | reverential 5.57142857143 590 | reverentially 5.57142857143 591 | reverently 5.57142857143 592 | revering 7.0 593 | revile 7.0 594 | revolt 2.0 595 | revolted 2.0 596 | rigid 4.6 597 | rigidly 4.6 598 | rigorously 4.6 599 | riot 2.3 600 | rioted 2.3 601 | rioter 2.14285714286 602 | rioters 2.3 603 | rioting 2.3 604 | riotous 3.0 605 | riotously 3.0 606 | riots 2.14285714286 607 | risky 4.5 608 | rollicking 3.0 609 | rowdy 3.0 610 | samzenpus 4.375 611 | scanlon 5.0 612 | schismatical 4.0 613 | sedition 3.0 614 | seditionists 3.0 615 | seditious 3.0 616 | semi-deserted 3.8 617 | semi-traditional 5.8 618 | serve 4.8 619 | serving 4.8 620 | slashdotter 4.375 621 | slightly 4.8 622 | slippery 3.6 623 | sobriquet 6.25 624 | sobriquets 6.25 625 | soldiery 4.75 626 | solemn 5.57142857143 627 | somewhat 4.8 628 | son 7.666666666669999 629 | sons 7.666666666669999 630 | soulskill 4.375 631 | spotlessness 7.0 632 | status 5.16666666667 633 | statuses 5.16666666667 634 | step-father 7.666666666669999 635 | stepfather 7.666666666669999 636 | stepmother 6.5 637 | stipend 5.25 638 | stipends 5.25 639 | stone-throwers 2.14285714286 640 | strife 2.4 641 | stringently 4.6 642 | stymie 4.0 643 | sub-caste 6.75 644 | sub-classify 6.0 645 | subcaste 6.75 646 | subclipse 1.2 647 | submission 6.0 648 | submissions 4.375 649 | submit 4.75 650 | submitted 6.0 651 | submitter 4.375 652 | submitting 6.0 653 | subservient 4.42857142857 654 | subversion 1.2 655 | subversive 1.2 656 | subversiveness 1.2 657 | subvert 3.0 658 | subverted 1.2 659 | subverting 1.2 660 | sullen 3.5 661 | sumbit 4.75 662 | super-polite 7.6 663 | superiority 6.833333333330001 664 | supermacy 6.833333333330001 665 | supremacy 6.833333333330001 666 | sympathiser 7.8 667 | sympathizer 7.8 668 | tender-heartedness 5.833333333330001 669 | thwart 3.0 670 | tidiness 7.0 671 | tortuous 3.6 672 | toserve 4.8 673 | totalitarian 8.2 674 | tradional 5.8 675 | tradition-minded 5.88888888889 676 | traditional 5.8 677 | traditional-minded 5.88888888889 678 | traditionalist 5.88888888889 679 | traitor 1.75 680 | traitorous 4.55555555556 681 | treacherous 3.6 682 | treacherously 3.6 683 | treachery 3.14285714286 684 | treason 2.642857142855 685 | treasonable 3.0 686 | treasonous 4.55555555556 687 | treasured 3.6 688 | treasuring 3.6 689 | troops 4.75 690 | truculent 3.5 691 | turncoat 2.2 692 | tyrannical 8.2 693 | ultra-polite 7.6 694 | ultra-traditional 5.88888888889 695 | unapologetic 3.5 696 | unauthoritative 7.833333333330001 697 | unbendingly 4.6 698 | uncivilized 2.16666666667 699 | unconcern 3.0 700 | uncowed 3.5 701 | undermine 3.0 702 | unfaithful 2.0 703 | unfaithfulness 2.28571428571 704 | ungoverned 2.16666666667 705 | uninhabited 3.8 706 | uninominal 4.0 707 | unionist 7.8 708 | unlawful 3.2 709 | unloyal 4.55555555556 710 | unpatriotic 4.55555555556 711 | unpleasantly 4.8 712 | unpoliced 2.16666666667 713 | untidiness 7.0 714 | upbraid 5.0 715 | upend 3.0 716 | uprising 3.16666666667 717 | uprisings 2.0 718 | uproarious 3.0 719 | usurp 3.0 720 | valorem 6.4 721 | venerate 7.0 722 | venerated 7.0 723 | venerating 6.666666666669999 724 | veneration 6.666666666669999 725 | vociferation 4.0 726 | well-mannered 7.6 727 | well-ordered 7.0 728 | womanliness 5.833333333330001 729 | womanly 5.833333333330001 730 | worshipful 5.57142857143 731 | -------------------------------------------------------------------------------- /moralstrength/annotations/v1.1/care.tsv: -------------------------------------------------------------------------------- 1 | WORD EXPRESSED_MORAL 2 | abandon 1.6 3 | abandoned 1.6 4 | abandoning 1.6 5 | abuse 2.5 6 | abusers 2.5 7 | abusing 2.5 8 | abusive 2.5 9 | admiration 8.57142857143 10 | afflict 2.6 11 | aghast 2.8 12 | amicability 5.42857142857 13 | amity 5.42857142857 14 | analytically 3.2 15 | angering 3.8333333333333295 16 | annihilate 1.33333333333 17 | annihilating 1.33333333333 18 | annihilation 1.33333333333 19 | anti-free-market 8.2 20 | anti-free-trade 6.8 21 | anti-market 8.2 22 | anti-trade 6.8 23 | anti-war 6.4 24 | antiwar 6.4 25 | appalled 2.8 26 | appanage 8.8 27 | appellant 3.2 28 | apple-cart 3.8333333333333295 29 | applecarts 3.8333333333333295 30 | assasins 1.0 31 | assassin 1.0 32 | assassinate 1.6 33 | assassination 1.0 34 | attack 2.66666666667 35 | attacked 2.66666666667 36 | attacking 2.66666666667 37 | bandage 2.2 38 | bang 1.0 39 | banged 1.0 40 | banging 1.0 41 | barbarity 3.4 42 | battle 4.0 43 | battles 4.0 44 | beggar-thy-neighbour 6.8 45 | belongings 8.8 46 | beneficial 2.4 47 | benefit 7.0 48 | benefited 7.0 49 | benefiting 7.0 50 | benefitted 7.0 51 | benefitting 7.0 52 | beneift 7.0 53 | bloodshed 1.66666666667 54 | borken 2.1666666666666696 55 | breaking 2.1666666666666696 56 | broken 2.1666666666666696 57 | brotherhood 5.42857142857 58 | brotherliness 5.42857142857 59 | brotherly 5.42857142857 60 | brutal 2.33333333333 61 | brutalise 1.2 62 | brutalising 1.2 63 | brutality 2.33333333333 64 | brutally 2.33333333333 65 | buck 1.0 66 | callously 1.6 67 | care 8.799999999999999 68 | careand 8.8 69 | caring 8.166666666669999 70 | civilian 7.8 71 | co-defendant 3.2 72 | codefendant 3.2 73 | codefendants 3.2 74 | collateral 3.8333333333333295 75 | collateralization 3.8333333333333295 76 | collateralizing 3.8333333333333295 77 | commiserate 8.0 78 | commiseration 8.57142857143 79 | compassion 7.0 80 | compassionate 8.166666666669999 81 | conciliator 8.6 82 | concord 5.42857142857 83 | condolences 8.57142857143 84 | conflict-prevention 7.8 85 | conserve 7.6 86 | conserving 7.4 87 | cordiality 5.42857142857 88 | counter-attack 2.66666666667 89 | counter-productive 2.4 90 | counterattack 2.66666666667 91 | counterproductive 2.4 92 | cracked 2.1666666666666696 93 | critical 3.2 94 | critically 3.2 95 | critically-endangered 4.6 96 | cruel 2.33333333333 97 | cruelly 1.6 98 | cruelness 3.4 99 | cruelties 3.4 100 | cruelty 3.4 101 | crush 2.4 102 | crushed 2.4 103 | crushee 2.4 104 | crusher 2.8 105 | crusherimpact 2.8 106 | crusherjaw 2.8 107 | crushers 2.8 108 | crushes 2.4 109 | crushing 2.4 110 | custodianship 8.8 111 | damage 3.0 112 | damaged 1.75 113 | damaging 2.4 114 | decapitate 1.6 115 | decimate 1.0 116 | defendant 3.2 117 | defenestrate 3.83333333333 118 | defenestrated 3.83333333333 119 | defenestration 3.83333333333 120 | defense 5.0 121 | defenseless 4.4 122 | defenselessly 4.4 123 | defensive 5.0 124 | deglobalization 6.8 125 | dehumanise 1.2 126 | deleterious 2.4 127 | demoralise 1.2 128 | depository 8.8 129 | deride 3.0 130 | despoil 3.4 131 | destory 1.0 132 | destroy 1.0 133 | destroyed 1.0 134 | destroying 1.0 135 | destruction 1.14285714285714 136 | detriment 7.0 137 | detrimental 2.4 138 | devastate 1.0 139 | devastation 1.14285714285714 140 | devour 3.4 141 | dignified 7.0 142 | dignity 7.0 143 | diminish 2.6 144 | disembowelment 3.83333333333 145 | disgusted 2.8 146 | dismayed 2.8 147 | disturbing 3.8333333333333295 148 | empathetic 6.8 149 | empathetically 6.8 150 | empathic 8.166666666669999 151 | empathise 8.0 152 | empathize 6.8 153 | empathized 8.0 154 | empathizes 8.0 155 | empathizing 6.8 156 | empathy 8.5 157 | endanger 2.6 158 | endangered 4.6 159 | endangering 4.6 160 | endangerment 1.8 161 | endure 2.6 162 | enmity 5.42857142857 163 | enraging 3.8333333333333295 164 | eschew 1.6 165 | exploit 2.83333333333 166 | exploitable 2.83333333333 167 | exploited 2.83333333333 168 | exploiting 2.83333333333 169 | extermination 1.33333333333 170 | extinction 1.33333333333 171 | fight 4.0 172 | fighting 4.0 173 | forego 1.6 174 | forsake 1.6 175 | forswear 3.0 176 | fought 4.0 177 | free-trade 6.8 178 | friendship 5.42857142857 179 | gaurd 6.8 180 | gaurds 6.8 181 | good-fellowship 5.42857142857 182 | guard 6.8 183 | guardian 8.57142857143 184 | guarding 6.8 185 | guardthe 6.8 186 | gurad 6.8 187 | harmed 2.28571428571 188 | harmful 2.4 189 | healthcare 8.6 190 | heartlessly 1.6 191 | heath-care 8.6 192 | heatlhcare 8.6 193 | helathcare 8.6 194 | helpess 4.4 195 | helpless 4.4 196 | hinder 2.6 197 | hippie 6.4 198 | hitman 1.0 199 | horrified 2.8 200 | humane 8.166666666669999 201 | huring 2.28571428571 202 | hurt 2.28571428571 203 | hurted 2.28571428571 204 | hurting 2.28571428571 205 | impair 2.6 206 | impaired 2.6 207 | impairing 2.6 208 | impare 2.6 209 | impede 2.6 210 | imperiled 4.6 211 | imperilled 4.6 212 | imperilment 1.8 213 | incensed 2.8 214 | inflict 2.6 215 | inflicted 2.6 216 | infuriated 2.8 217 | inhibit 2.6 218 | inhumanity 3.4 219 | injure 2.28571428571 220 | injurious 2.4 221 | intruding 2.6 222 | intrusion 2.6 223 | intrusive 2.6 224 | intrusively 2.6 225 | intrusiveness 2.6 226 | invasive 2.6 227 | isolationism 6.8 228 | jeopardize 1.4 229 | jettison 1.6 230 | kill 1.6 231 | killed 1.6 232 | killer 1.0 233 | killing 1.6 234 | kind-hearted 8.166666666669999 235 | kindhearted 8.166666666669999 236 | kindness 7.0 237 | law-abiding 7.857142857139999 238 | maim 1.6 239 | maintaining 7.4 240 | mercantilist 6.8 241 | merciless 2.33333333333 242 | mercilessly 1.6 243 | murderer 1.0 244 | murderous 2.83333333333 245 | near-threatened 4.6 246 | non-collateral 3.8333333333333295 247 | non-healthcare 8.6 248 | non-judgmental 8.166666666669999 249 | non-sympathetic 8.333333333330001 250 | non-violent 7.857142857139999 251 | non-war 7.8 252 | nonmilitary 7.8 253 | nonviolent 7.857142857139999 254 | objectively 3.2 255 | obliterate 1.0 256 | obliteration 1.33333333333 257 | obtrusive 2.6 258 | offense 5.0 259 | offriendship 5.42857142857 260 | opponent 3.2 261 | outraged 2.8 262 | overbearing 2.6 263 | overlordship 8.8 264 | pacifist 6.4 265 | pacifists 6.4 266 | peacably 7.857142857139999 267 | peace 7.8 268 | peace-building 7.8 269 | peace-enforcement 7.8 270 | peace-keeper 8.6 271 | peace-keepers 7.8 272 | peace-loving 7.857142857139999 273 | peace-makers 8.6 274 | peace-making 8.6 275 | peaceable 7.857142857139999 276 | peaceful 7.8 277 | peacekeeper 7.8 278 | peacekeepers 7.8 279 | peacekeeping 7.8 280 | peacemaker 8.6 281 | peacemaking 8.6 282 | peacenik 6.4 283 | peacetime 7.8 284 | perserve 7.6 285 | perserving 7.4 286 | pillage 3.4 287 | plaintiff 3.2 288 | post-war 7.8 289 | postwar 7.8 290 | preservation 7.6 291 | preserve 7.6 292 | preserves 7.4 293 | preserving 7.4 294 | pro-peace 6.4 295 | pro-tect 6.0 296 | pro-trade 8.2 297 | protected 8.0 298 | protecters 8.57142857143 299 | protecting 8.57142857143 300 | protection 8.0 301 | protectionism 6.8 302 | protectionist 8.2 303 | protectionists 6.8 304 | protective 8.57142857143 305 | protector 8.57142857143 306 | protectorship 8.8 307 | pulverize 2.4 308 | pulverizer 2.8 309 | pummel 3.4 310 | quarell 1.8333333333333304 311 | quarrel 1.8333333333333304 312 | quarreled 1.8333333333333304 313 | quarreling 1.8333333333333304 314 | quarrelled 1.8333333333333304 315 | quarrelling 1.8333333333333304 316 | ravage 3.4 317 | ravaged 1.75 318 | ravaging 3.4 319 | ravish 3.4 320 | rebuff 3.0 321 | reconciler 8.6 322 | refuge 6.75 323 | reignty 8.8 324 | reject 3.0 325 | relinquish 1.6 326 | renounce 1.6 327 | restoring 7.4 328 | ruin 1.4 329 | ruination 1.4 330 | ruined 1.4 331 | ruining 1.4 332 | ruthless 2.33333333333 333 | ruthlessly 1.6 334 | safe 8.6 335 | safe-guarding 6.0 336 | safe-haven 6.75 337 | safe-ish 8.6 338 | safe-keep 8.8 339 | safeand 8.6 340 | safegaurd 6.0 341 | safeguard 6.0 342 | safeguarded 6.0 343 | safeguarding 6.0 344 | safehaven 6.75 345 | safehold 6.6 346 | safekeep 8.8 347 | safekeeping 8.8 348 | safely 8.6 349 | safest 8.6 350 | safety 8.2 351 | saftey 8.2 352 | sanctuary 6.75 353 | savage 2.33333333333 354 | savagely 1.6 355 | secruity 8.2 356 | secuirty 8.2 357 | security 8.2 358 | securtiy 8.2 359 | secutiry 8.2 360 | self-annihilation 1.33333333333 361 | self-critically 3.2 362 | self-dignity 7.0 363 | self-respect 7.0 364 | severely 3.2 365 | shattered 2.1666666666666696 366 | sheild 7.5 367 | sheilds 7.5 368 | shelter 7.0 369 | sheltering 7.0 370 | shelterless 7.0 371 | shield 7.5 372 | shielded 7.5 373 | shielding 7.5 374 | shiled 7.5 375 | shocked 2.8 376 | shun 3.0 377 | siitne 2.8 378 | smashed 1.75 379 | solace 6.75 380 | sove- 8.8 381 | sover- 8.8 382 | spoil 1.4 383 | spurn 3.0 384 | spurned 3.0 385 | spurning 3.0 386 | squabble 1.8333333333333304 387 | state-listed 4.6 388 | stomp 2.33333333333 389 | stomped 2.33333333333 390 | stompers 2.33333333333 391 | stompin 2.33333333333 392 | stomping 2.33333333333 393 | strongbox 8.8 394 | suffer 2.6 395 | suffered 2.6 396 | suffering 2.6 397 | sufferred 2.6 398 | suzerain 8.8 399 | suzerainty 8.8 400 | symapthy 8.57142857143 401 | sympathetic 8.333333333330001 402 | sympathetically 8.333333333330001 403 | sympathic 8.333333333330001 404 | sympathies 8.57142857143 405 | sympathize 8.0 406 | sympathizing 8.333333333330001 407 | sympathy 8.57142857143 408 | threatened 4.6 409 | trample 2.33333333333 410 | tranquility 7.8 411 | transferral 8.8 412 | trashed 1.75 413 | traumatise 1.2 414 | tromp 2.33333333333 415 | troop-contributing 7.8 416 | tyrannise 1.2 417 | ultra-violent 2.83333333333 418 | unarmed 4.4 419 | undefended 4.4 420 | undermine 2.6 421 | unhealing 2.2 422 | unintrusive 2.6 423 | unityit 2.8 424 | unmercifully 1.6 425 | unsettling 3.8333333333333295 426 | unsympathetic 8.333333333330001 427 | upset 3.8333333333333295 428 | upsets 3.8333333333333295 429 | upsetting 3.8333333333333295 430 | vicious 2.33333333333 431 | viciously 1.6 432 | viciousness 3.4 433 | victimise 1.2 434 | viloent 2.83333333333 435 | violence 1.66666666667 436 | violent 2.83333333333 437 | viscountcy 8.8 438 | voilent 2.83333333333 439 | vulnerable 4.4 440 | war 1.66666666667 441 | war-fighting 7.8 442 | war-hawk 6.4 443 | war-monger 6.4 444 | war-time 7.8 445 | wardenship 8.8 446 | warlike 7.857142857139999 447 | warmonger 6.4 448 | wartime 7.8 449 | weaponless 4.4 450 | wound 2.2 451 | wounds 2.2 452 | wreck 1.75 453 | wrecked 1.75 454 | wrecking 1.75 455 | wrecks 1.75 456 | -------------------------------------------------------------------------------- /moralstrength/annotations/v1.1/fairness.tsv: -------------------------------------------------------------------------------- 1 | WORD EXPRESSED_MORAL 2 | 15th-minute 6.2 3 | 42nd-minute 6.2 4 | 56th-minute 6.2 5 | abhorrent 4.66666666666667 6 | accept 7.83333333333333 7 | acceptance 7.83333333333333 8 | accepted 7.83333333333333 9 | accepting 7.83333333333333 10 | accepts 7.83333333333333 11 | accpeting 7.83333333333333 12 | accurate 4.4 13 | acknowledging 7.83333333333333 14 | action-guiding 5.5 15 | agreeable 6.2 16 | amiable 6.2 17 | amplitude 5.16666666666667 18 | anti-black 2.8 19 | anti-consumerism 5.5 20 | anti-discriminatory 4.0 21 | anti-egalitarian 6.0 22 | anti-gay 4.0 23 | anti-homosexual 4.0 24 | anti-integration 2.8 25 | anti-segregation 2.8 26 | anti-southern 2.2 27 | antigay 4.0 28 | avaricious 2.7142857142900003 29 | balance 5.6 30 | balancing 5.6 31 | barbaric 4.66666666666667 32 | bias 5.2 33 | bias-free 8.6 34 | biased 5.2 35 | biases 5.2 36 | biasing 5.2 37 | biasness 5.2 38 | bigot 3.6 39 | bigoted 3.6 40 | 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4.0 81 | discriminate 4.0 82 | discriminated 4.0 83 | discriminates 4.0 84 | discrimination 4.0 85 | discriminative 4.0 86 | discriminatory 4.0 87 | discursive 5.5 88 | dishonest 5.2 89 | dishonestly 5.2 90 | disingenuous 5.2 91 | disparity 3.16666666667 92 | dispassionate 7.166666666669999 93 | disportionate 3.6 94 | disproportion 3.16666666667 95 | disproportional 3.16666666667 96 | disproportionality 3.16666666667 97 | disproportionally 3.6 98 | disproportionate 3.6 99 | disproportionately 3.28571428571429 100 | disproportioned 3.16666666667 101 | disqualify 4.2 102 | disreputable 2.7142857142900003 103 | dissociate 3.83333333333 104 | dissociating 3.83333333333 105 | dissociation 3.83333333333 106 | dixiecrat 2.2 107 | dixiecrats 2.8 108 | duplicitous 5.2 109 | egalitarian 6.0 110 | egalitarianism 6.0 111 | enslavement 3.6666666666666696 112 | epistemic 5.5 113 | eqaul 7.0 114 | equable 6.2 115 | equal 7.0 116 | equaling 7.0 117 | equaliser 6.2 118 | equalitarian 5.33333333333 119 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275 | oscillation 5.16666666666667 276 | oscillations 5.16666666666667 277 | out-of-proportion 3.6 278 | over-consumption 5.5 279 | over-represented 3.28571428571429 280 | overconsumption 5.5 281 | overrepresented 3.28571428571429 282 | peak-to-peak 5.16666666666667 283 | peerless 3.66666666667 284 | perference 5.4 285 | permanance 5.16666666666667 286 | permanency 5.16666666666667 287 | permanent 5.16666666666667 288 | phase-delay 5.16666666666667 289 | placid 6.2 290 | pre-judgement 4.125 291 | pre-judgments 4.125 292 | pre-verdict 4.125 293 | preference 5.4 294 | preferred 5.4 295 | prejudgements 4.125 296 | prejudgment 4.125 297 | prejudice 3.83333333333 298 | prejudiced 6.0 299 | prejudicial 6.0 300 | prejudicially 6.0 301 | prejudicing 6.0 302 | pro-segregation 2.2 303 | probity 8.71428571429 304 | proficient 6.4 305 | proportion 3.16666666667 306 | proportional 3.0 307 | proportionality 3.0 308 | proportionally 3.28571428571429 309 | proportionate 3.6 310 | proportionately 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sincerity 7.666666666669999 350 | skilled 6.4 351 | slave-holding 3.6666666666666696 352 | slave-ownership 3.6666666666666696 353 | slavery 3.6666666666666696 354 | slaves 3.6666666666666696 355 | slavocracy 2.2 356 | smoothness 5.16666666667 357 | solidaristic 5.33333333333 358 | somewhat 4.875 359 | steady-going 6.2 360 | stereotyping 3.83333333333 361 | stoppage-time 6.2 362 | subjective 5.5 363 | subjectively 5.5 364 | subjectiveness 5.5 365 | subjectivity 5.5 366 | subjugation 5.75 367 | subpulse 5.16666666666667 368 | surprisingly 4.875 369 | temperate 6.2 370 | temporariness 5.16666666666667 371 | theright 8.166666666669999 372 | tolerance 7.166666666669999 373 | tolerant 7.166666666669999 374 | transitory 5.16666666666667 375 | truthful 7.2 376 | truthfulness 7.666666666669999 377 | tyranny 5.75 378 | un-biased 8.0 379 | unbaised 8.0 380 | unbias 8.0 381 | unbiased 8.6 382 | unbiassed 8.0 383 | unbigoted 7.166666666669999 384 | unblinkered 6.71428571429 385 | unceasing 4.5 386 | unconscionable 4.66666666666667 387 | unequal 4.5 388 | unequalised 4.0 389 | unequality 4.5 390 | unequalled 3.66666666667 391 | unequally 4.5 392 | unethical 5.2 393 | unevenness 5.16666666667 394 | unfair 4.625 395 | unfairly 4.625 396 | unfairness 6.5 397 | unfare 4.625 398 | unfounded 4.625 399 | uniformity 5.16666666667 400 | unjust 6.333333333330001 401 | unjustifiable 6.333333333330001 402 | unjustifiably 4.625 403 | unjustified 4.625 404 | unjustly 5.4 405 | unjustness 6.5 406 | unlawfully 5.4 407 | unlikeness 3.16666666667 408 | unmatched 3.66666666667 409 | unparalled 3.66666666667 410 | unparalleled 3.66666666667 411 | unprejudiced 6.71428571429 412 | unprincipled 2.7142857142900003 413 | unproportional 3.6 414 | unreasonable 5.0 415 | unrighteous 6.333333333330001 416 | unrightfully 5.4 417 | unrivaled 3.66666666667 418 | unrivalled 3.66666666667 419 | unruffled 6.2 420 | unscrupulous 2.7142857142900003 421 | unsurpassed 3.66666666667 422 | untruthful 5.2 423 | unvarying 6.2 424 | unwarranted 4.625 425 | unwarrented 4.625 426 | waveform 5.16666666666667 427 | waveshape 5.16666666666667 428 | wrong 8.166666666669999 429 | wrongfully 5.4 430 | wrongly 5.4 431 | -------------------------------------------------------------------------------- /moralstrength/annotations/v1.1/lexicons_expanded_v1-1.pck: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oaraque/moral-foundations/2be815db653ec769587e53eb46557b7bfc87058a/moralstrength/annotations/v1.1/lexicons_expanded_v1-1.pck -------------------------------------------------------------------------------- /moralstrength/annotations/v1.1/loyalty.tsv: -------------------------------------------------------------------------------- 1 | WORD EXPRESSED_MORAL 2 | abandon 1.42857142857 3 | abandoned 1.42857142857 4 | abandoning 1.42857142857 5 | acolytes 6.0 6 | admirer 7.2 7 | adoringly 7.0 8 | affectionately 7.0 9 | aficionado 7.2 10 | age-cohort 6.0 11 | agmut 6.0 12 | allegence 8.9 13 | allegiance 8.9 14 | alleigance 8.9 15 | alliegance 8.9 16 | alliegence 8.9 17 | allies 8.71428571429 18 | allies- 8.71428571429 19 | alligence 8.9 20 | ally 8.71428571429 21 | amicability 7.857142857142861 22 | amity 7.857142857142861 23 | anti-black 4.5 24 | anti-integration 4.5 25 | anti-nationalist 6.0 26 | anti-patriot 7.857142857139999 27 | anti-segregation 4.5 28 | anti-southern 3.14285714286 29 | antiphonally 7.5 30 | apostacy 4.2 31 | apostasize 3.8 32 | apostasy 3.8 33 | apostate 4.2 34 | apostates 3.8 35 | apostatized 3.8 36 | apostatizing 3.8 37 | archenemy 8.71428571429 38 | assiduity 8.0 39 | bandit 1.5 40 | barbarity 3.3333333333333304 41 | beguile 1.33333333333 42 | behind-the-curtain 5.625 43 | belittling 3.5 44 | betrayal 1.0 45 | betrayed 1.0 46 | betrayer 1.0 47 | betrayers 1.8 48 | betraying 1.0 49 | bhakta 7.2 50 | brigand 1.5 51 | brigands 1.5 52 | brotherhood 7.857142857142861 53 | brotherliness 7.857142857142861 54 | brotherly 7.857142857142861 55 | brutality 3.3333333333333304 56 | cadre 6.0 57 | call-and-answer 7.5 58 | camaderie 7.57142857143 59 | camaraderie 7.57142857143 60 | cameraderie 7.57142857143 61 | camraderie 7.57142857143 62 | caringly 7.0 63 | ceo 6.375 64 | chearfulness 8.0 65 | cheated-on 2.375 66 | choir-like 7.5 67 | chorus 7.5 68 | chorused 7.5 69 | clannish 5.4 70 | clap 7.5 71 | clap-clap-clap 7.5 72 | claping 7.5 73 | clapped 7.5 74 | clapping 7.5 75 | clique 5.88888888889 76 | clique-ish 5.4 77 | clique-y 5.88888888889 78 | cliques 5.4 79 | cliquey 5.4 80 | cliquish 5.4 81 | cliquishness 5.88888888889 82 | co-founder 6.375 83 | co-founders 6.375 84 | cofounder 6.375 85 | cohort 6.0 86 | cohort-based 6.0 87 | collective 7.333333333330001 88 | collectively 7.333333333330001 89 | collectivism 7.1111111111100005 90 | collectivist 6.4 91 | collectivistic 7.1111111111100005 92 | collectivists 7.1111111111100005 93 | collectivity 7.333333333330001 94 | comaraderie 7.57142857143 95 | commie 3.85714285714 96 | commitment 6.4 97 | committment 6.4 98 | communal 6.2 99 | communal-style 6.2 100 | communality 6.2 101 | communally 6.2 102 | communard 5.375 103 | communards 5.375 104 | commune 5.375 105 | communism 4.42857142857 106 | communist 3.85714285714 107 | communistic 3.85714285714 108 | communists 4.42857142857 109 | communitarian 6.4 110 | communitarianism 7.1111111111100005 111 | communities 6.2 112 | community 6.2 113 | communtiy 6.2 114 | compatriot 7.1428571428600005 115 | compatriots 7.1428571428600005 116 | comradely 7.57142857143 117 | comraderie 7.57142857143 118 | comradery 7.57142857143 119 | comradeship 7.57142857143 120 | concord 7.857142857142861 121 | confidant 8.71428571429 122 | confidante 8.71428571429 123 | constitutionalist 7.857142857139999 124 | consular 6.33333333333333 125 | consulate 6.33333333333333 126 | consulate-general 6.33333333333333 127 | cordiality 7.857142857142861 128 | corps 6.0 129 | coterie 6.0 130 | coteries 5.88888888889 131 | country-wide 6.0 132 | countrywide 6.0 133 | coward 3.25 134 | cowardice 3.25 135 | cowardliness 3.25 136 | cowardly 3.25 137 | cowardness 3.25 138 | cowards 3.25 139 | cruel 3.3333333333333304 140 | cruelness 3.3333333333333304 141 | cruelties 3.3333333333333304 142 | cruelty 3.3333333333333304 143 | customised 4.0 144 | dangerous 1.57142857143 145 | de-segregation 4.75 146 | deceit 1.125 147 | deceive 1.33333333333 148 | deceiving 1.33333333333 149 | deception 1.125 150 | deceptions 1.125 151 | deciet 1.125 152 | decieve 1.33333333333 153 | dedicate 8.0 154 | dedicated 8.0 155 | dedicates 8.0 156 | dedicating 8.0 157 | dedication 6.4 158 | defraud 1.33333333333 159 | delicately 7.0 160 | delude 1.33333333333 161 | demeaning 3.5 162 | demoralise 1.6 163 | denigrating 3.5 164 | desegregation 4.75 165 | deserted 2.77777777778 166 | desertion 1.83333333333 167 | desolate 2.77777777778 168 | desolated 2.77777777778 169 | desperado 1.5 170 | devious 1.57142857143 171 | devo- 8.0 172 | devoted 8.0 173 | devotedness 8.0 174 | devotedto 8.0 175 | devotee 7.2 176 | devoting 8.0 177 | devotion 8.0 178 | diehard 7.555555555560001 179 | disgruntled 2.375 180 | dishonesty 1.125 181 | disloyal 1.85714285714 182 | disloyalty 1.83333333333 183 | disrespectful 3.5 184 | disrespecting 3.5 185 | disunited 8.5 186 | dixiecrat 3.14285714286 187 | dixiecrats 4.5 188 | doppelganger 2.28571428571 189 | duplicitous 1.57142857143 190 | duplicity 1.125 191 | embassey 6.33333333333333 192 | embassy 6.33333333333333 193 | embittered 2.375 194 | enemy 8.71428571429 195 | enmity 7.857142857142861 196 | eschew 1.42857142857 197 | espionage 2.875 198 | ethno-national 7.0 199 | ethno-nationalism 6.83333333333333 200 | ethno-nationalist 6.0 201 | ethnonationalist 6.0 202 | ever-loyal 8.875 203 | ex-lover 2.375 204 | faithful 8.875 205 | faithlessness 1.83333333333 206 | fake 2.28571428571 207 | faker 2.28571428571 208 | families 8.0 209 | family 8.0 210 | famliy 8.0 211 | fanatic 7.2 212 | fatherland 7.1428571428600005 213 | fealty 8.9 214 | fellow 7.1428571428600005 215 | fellowship 8.166666666669999 216 | fiber-cement 7.5 217 | fmaily 8.0 218 | foe 8.71428571429 219 | foregin 4.5 220 | forego 1.42857142857 221 | foreign 4.5 222 | foreigner 4.5 223 | foreing 4.5 224 | foriegn 4.5 225 | forsake 1.42857142857 226 | forsaken 2.77777777778 227 | founder 6.375 228 | freedom-fighter 7.857142857139999 229 | friends 8.0 230 | friendship 7.857142857142861 231 | functionaries 6.0 232 | gently 7.0 233 | good-fellowship 7.57142857143 234 | gorup 6.375 235 | group 6.375 236 | gruop 6.375 237 | guild 7.1428571428600005 238 | guildies 7.1428571428600005 239 | guildmaster 7.1428571428600005 240 | guildmembers 7.1428571428600005 241 | gutlessness 3.25 242 | half-deserted 2.77777777778 243 | handclap 7.5 244 | hard-work 6.4 245 | hardieplank 7.5 246 | hardiplank 7.5 247 | harmony 7.5 248 | heavenly-mindedness 8.0 249 | heimat 7.1428571428600005 250 | heresy 3.8 251 | highwayman 1.5 252 | hippie-style 5.375 253 | homeland 7.1428571428600005 254 | hoodwink 1.33333333333 255 | hyper-individualistic 3.8 256 | hyper-nationalism 6.83333333333333 257 | immigrants 4.83333333333 258 | immigration 4.83333333333 259 | imposter 2.28571428571 260 | impostors 2.28571428571 261 | in-crowd 5.88888888889 262 | in-synch 7.5 263 | individual 4.28571428571 264 | individualisation 4.0 265 | individualised 4.0 266 | individualising 4.0 267 | individualism 7.1111111111100005 268 | individualist 6.4 269 | individualistic 3.8 270 | individualistically 3.8 271 | individualists 3.8 272 | individually-tailored 4.0 273 | indiviudal 4.28571428571 274 | indivual 4.28571428571 275 | infatuation 4.42857142857143 276 | inhumanity 3.3333333333333304 277 | insatiable 4.42857142857143 278 | insider 5.625 279 | insider-only 5.625 280 | insider-y 5.625 281 | insubordinate 1.85714285714 282 | insular 5.4 283 | insult 3.5 284 | insulted 3.5 285 | insulting 3.5 286 | integrationism 3.14285714286 287 | international 5.2 288 | jettison 1.42857142857 289 | jihadist 2.66666666667 290 | jilt 2.375 291 | jilted 2.375 292 | jilting 2.375 293 | jilts 2.375 294 | jingoism 6.83333333333333 295 | joint 6.833333333330001 296 | kinism 3.14285714286 297 | lieutenants 6.0 298 | like-minded 7.1428571428600005 299 | likeminded 7.1428571428600005 300 | local 5.2 301 | long-time 7.1428571428600005 302 | lovingly 7.0 303 | loyal 8.875 304 | loyalest 8.875 305 | loyalist 7.555555555560001 306 | loyally 8.875 307 | loyalties 7.555555555560001 308 | loyalty 7.555555555560001 309 | lust 4.42857142857143 310 | lustful 4.42857142857143 311 | lusting 4.42857142857143 312 | lustings 4.42857142857143 313 | malefactor 2.8 314 | malefactors 2.8 315 | malfeasant 2.8 316 | marauder 1.5 317 | marxism 4.42857142857 318 | marxist 3.85714285714 319 | marxist-leninist 3.85714285714 320 | maverick 4.16666666667 321 | member 6.625 322 | memebr 6.625 323 | miscreant 2.8 324 | misinform 1.33333333333 325 | mislead 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2.28571428571 365 | polytrichum 5.375 366 | post-nationalist 7.0 367 | pretender 2.28571428571 368 | pro-segregation 3.14285714286 369 | pusillanimity 3.25 370 | racialism 3.14285714286 371 | rapscallion 2.8 372 | rapscallions 2.8 373 | rascally 2.8 374 | re-nationalisation 5.6 375 | re-segregation 4.75 376 | re-sided 7.5 377 | recidivist 2.8 378 | regional 5.2 379 | relinquish 1.42857142857 380 | renationalisation 5.6 381 | renegade 4.16666666667 382 | renounce 1.42857142857 383 | resegregation 4.75 384 | reverently 7.0 385 | rhythmically 7.5 386 | ridiculing 3.5 387 | robber 1.5 388 | rogue 2.6666666666666696 389 | roguish 4.16666666667 390 | roofing 7.5 391 | rouge 2.6666666666666696 392 | saint-benoît 5.375 393 | scofflaw 2.8 394 | segregated 4.75 395 | segregating 4.75 396 | segregation 4.75 397 | segregation-era 4.5 398 | segregationism 3.14285714286 399 | segregationist 4.5 400 | segregationists 3.14285714286 401 | self-devotion 8.0 402 | self-expressive 3.8 403 | self-oriented 3.8 404 | self-styled 4.16666666667 405 | semi-deserted 2.77777777778 406 | separationism 3.14285714286 407 | sequester 4.71428571429 408 | sequestering 4.71428571429 409 | sequestration 4.71428571429 410 | sequestration-related 4.71428571429 411 | siding 7.5 412 | single-hearted 8.0 413 | slavocracy 3.14285714286 414 | slippery 1.57142857143 415 | snobbish 5.4 416 | socialism 7.1111111111100005 417 | socialist 6.4 418 | socialistic 6.4 419 | solidaristic 8.6 420 | solidarities 8.6 421 | solidarity 8.6 422 | solidarity-based 8.6 423 | solidary 8.6 424 | spies 2.875 425 | spinelessness 3.25 426 | spy 2.875 427 | spying 2.875 428 | spymaster 2.875 429 | stalinism 4.42857142857 430 | stalinist 3.85714285714 431 | state-formation 7.0 432 | state-wide 6.0 433 | statewide 6.0 434 | statism 7.1111111111100005 435 | statist 6.4 436 | sub-cohort 6.0 437 | sub-group 6.375 438 | subcohort 6.0 439 | subcohorts 6.0 440 | subgroup 6.375 441 | subterfuge 1.125 442 | super-patriot 7.857142857139999 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1.83333333333 481 | unguilded 7.1428571428600005 482 | unified 8.5 483 | unify 8.5 484 | unifying 8.5 485 | uninhabited 2.77777777778 486 | unionist 7.555555555560001 487 | unison 7.5 488 | united 8.5 489 | uniting 8.5 490 | unity 8.6 491 | unloyal 1.85714285714 492 | unpatriotic 1.85714285714 493 | unwearied 8.0 494 | viciousness 3.3333333333333304 495 | victimise 1.6 496 | votary 7.2 497 | votion 8.0 498 | wayward 4.16666666667 499 | whole-heartedness 8.0 500 | worshiper 7.2 501 | worshipper 7.2 502 | wronged 2.375 503 | -------------------------------------------------------------------------------- /moralstrength/annotations/v1.1/purity.tsv: -------------------------------------------------------------------------------- 1 | WORD EXPRESSED_MORAL 2 | -stained 5.16666666667 3 | Catholics 6.0 4 | abhorrent 2.42857142857 5 | abject 3.0 6 | abjectness 3.0 7 | abominable 1.875 8 | abstinence 7.714285714289999 9 | abstinence-based 7.714285714289999 10 | abstinence-only 7.714285714289999 11 | abstinence-until-marriage 7.714285714289999 12 | abstinent 7.714285714289999 13 | acrophobia 4.8 14 | adulterant 2.83333333333 15 | adulterants 2.83333333333 16 | adulterated 2.83333333333 17 | adulterating 2.83333333333 18 | adulteration 2.83333333333 19 | adulterations 2.83333333333 20 | adulterer 1.5 21 | adulterine 4.11111111111 22 | adulterous 1.5 23 | adultery 1.5 24 | air-freshener 7.83333333333333 25 | airfreshner 7.83333333333333 26 | amoral 3.25 27 | anti-austerity 5.666666666669999 28 | anti-catholics 6.0 29 | antique-like 3.3333333333333304 30 | antiseptic 7.8 31 | apostacy 3.5 32 | apostasize 3.2 33 | apostasy 3.2 34 | apostate 3.5 35 | apostates 3.2 36 | apostatized 3.2 37 | apostatizing 3.2 38 | appalling 2.25 39 | arch-bishop 7.0 40 | arch-heretic 2.5714285714300003 41 | archdeacon 7.0 42 | asceticism 7.42857142857 43 | aseptic 7.8 44 | atheistical 3.2 45 | austerians 5.666666666669999 46 | austerities 5.666666666669999 47 | austerity 5.666666666669999 48 | austerity-driven 5.666666666669999 49 | austerity-lite 5.666666666669999 50 | bacchanalia 2.2 51 | bacchanalian 2.2 52 | backslider 2.42857142857 53 | barbarous 3.2 54 | baseness 3.0 55 | bashfulness 8.166666666669999 56 | beatification 8.0 57 | beatified 8.0 58 | bed-hopping 3.14285714286 59 | beggary 3.0 60 | belt-tightening 5.666666666669999 61 | besmirch 3.66666666667 62 | bishop 7.0 63 | blacken 3.66666666667 64 | blackguardly 2.6 65 | bland 5.33333333333333 66 | blas- 3.5714285714300003 67 | blasphemer 2.5714285714300003 68 | blasphemous 3.2 69 | blemish 4.0 70 | blemished 4.0 71 | blemishes 4.0 72 | blemishing 4.0 73 | blotch 4.0 74 | bppv 4.8 75 | brazen 2.6 76 | brothels 3.28571428571 77 | burnish 3.66666666667 78 | canonisation 8.0 79 | canonization 8.0 80 | canonizing 8.0 81 | capableness 8.333333333330001 82 | catholic 6.0 83 | catholicism 6.0 84 | celebacy 7.625 85 | celebate 8.0 86 | celibacy 8.0 87 | celibate 7.625 88 | celibates 8.0 89 | cesspit 2.2 90 | cesspool 2.2 91 | chaste 7.57142857143 92 | chastely 7.57142857143 93 | chasten 6.5 94 | chastened 6.5 95 | chasteness 7.57142857143 96 | chasteneth 6.5 97 | chastening 6.5 98 | chastens 6.5 99 | chastisement 6.5 100 | chastity 8.0 101 | chruch 6.833333333330001 102 | church 6.833333333330001 103 | church-attending 7.4 104 | church-goer 5.42857142857 105 | church-goers 7.4 106 | church-going 7.4 107 | church-member 7.4 108 | churches 6.833333333330001 109 | churchgoer 7.4 110 | churchgoers 5.42857142857 111 | churchgoing 5.42857142857 112 | churchman 7.0 113 | claen 7.666666666669999 114 | clean 7.666666666669999 115 | clean-ups 6.5 116 | cleanand 7.666666666669999 117 | cleaner 7.5 118 | cleaniless 7.6 119 | cleaning-up 6.5 120 | cleanliness 7.6 121 | cleanness 7.6 122 | cleanse 8.0 123 | cleansed 8.0 124 | cleanses 8.0 125 | cleansing 8.0 126 | cleanup 6.5 127 | clergyman 7.0 128 | clergymen 7.0 129 | concupiscence 2.66666666667 130 | congregation 6.833333333330001 131 | conserve 6.833333333330001 132 | contagion 2.7142857142900003 133 | contagious 3.5 134 | contagiously 3.5 135 | contagiousness 2.7142857142900003 136 | contaminated 2.83333333333 137 | courtezan 2.6 138 | crass 5.33333333333333 139 | crossbred 6.5 140 | cumslut 2.75 141 | cunt 2.75 142 | cussing 2.6 143 | cusswords 2.6 144 | debauched 2.2 145 | debauchee 2.6 146 | debaucheries 2.2 147 | debaucherous 2.2 148 | debauchery 2.2 149 | decent 8.0 150 | decent-enough 8.0 151 | decent-ish 8.0 152 | decentalisation 5.0 153 | decorum 8.166666666669999 154 | defilement 2.76923076923 155 | defiling 2.75 156 | deflowered 7.555555555560001 157 | dejection 3.0 158 | deluded 3.0 159 | deluding 3.0 160 | delusion 3.0 161 | delusional 3.0 162 | delusionary 3.0 163 | delusioned 3.0 164 | demure 7.57142857143 165 | demureness 8.166666666669999 166 | deodorizer 7.83333333333333 167 | depraved 3.25 168 | depravity 3.25 169 | desecrate 2.75 170 | desecrated 2.75 171 | desecrates 2.75 172 | desecrating 2.75 173 | desecration 2.75 174 | desecrators 2.75 175 | despicable 3.25 176 | destitution 3.0 177 | detestably 5.0 178 | devastate 2.75 179 | deviancy 1.7142857142857095 180 | deviant 3.25 181 | devilish 1.85714285714 182 | devout 7.0 183 | devoutness 7.42857142857 184 | diabolical 1.85714285714 185 | dirt 2.375 186 | dirtier 7.5 187 | dirtiest 3.0 188 | dirtily 3.22222222222 189 | dirtiness 2.2 190 | dirty 3.0 191 | dirtyness 2.76923076923 192 | discolored 2.6 193 | disease 3.0 194 | disgraceful 2.25 195 | disgusting 2.0 196 | disgustingly 5.0 197 | disgustingness 2.16666666667 198 | disreputably 3.22222222222 199 | dissolute 2.6 200 | dissoluteness 3.5714285714300003 201 | distasteful 2.42857142857 202 | dizziness 4.8 203 | dizzy 4.8 204 | dizzyness 4.8 205 | dreadful 1.875 206 | drunkeness 2.2 207 | dust 2.375 208 | ecclesiastic 7.0 209 | endanger 2.75 210 | epidemics 2.7142857142900003 211 | erectly 6.0 212 | evil 1.85714285714 213 | ex-priest 7.2 214 | excommunicated 2.5714285714300003 215 | excrement 2.2 216 | 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7.0 258 | harlot 1.57142857143 259 | healthful 8.4 260 | healthy 8.4 261 | hedonism 2.2 262 | hell-deserving 2.42857142857 263 | heresies 2.5714285714300003 264 | heresy 3.2 265 | heretic 2.5714285714300003 266 | heretical 2.5714285714300003 267 | hereticks 3.2 268 | heritics 2.5714285714300003 269 | hideousness 2.16666666667 270 | hobo 3.0 271 | holies 8.5 272 | holiest 8.5 273 | holiness 8.5 274 | holy 8.5 275 | horribleness 2.16666666667 276 | horrid 1.875 277 | horrific 2.25 278 | horrifying 2.25 279 | hussy 2.75 280 | hygeine 5.857142857142861 281 | hygiene 5.857142857142861 282 | hygienic 5.857142857142861 283 | hymnologist 7.0 284 | ickiness 2.16666666667 285 | icky-ness 2.16666666667 286 | ill 2.7142857142900003 287 | illness 3.0 288 | immaculate 7.0 289 | immoderate 4.28571428571 290 | immodest 2.8 291 | immodesty 8.166666666669999 292 | imperfection 4.0 293 | impetuous 4.28571428571 294 | impieties 3.5714285714300003 295 | impiety 3.5714285714300003 296 | impious 3.2 297 | impiously 3.5714285714300003 298 | improvident 2.85714285714 299 | impurity 7.1111111111100005 300 | indecency 2.8 301 | indecent 2.8 302 | infectious 3.5 303 | infectous 3.5 304 | infectuous 3.5 305 | infidelity 1.5 306 | injudicious 4.28571428571 307 | innocence 8.4 308 | innocent 8.4 309 | inoccent 8.4 310 | insipid 5.33333333333333 311 | integrity 7.8 312 | intemperance 4.28571428571 313 | intemperate 4.28571428571 314 | intergity 7.8 315 | inviolable 8.5 316 | irreligion 3.5714285714300003 317 | jeopardize 2.75 318 | judaizer 2.5714285714300003 319 | labyrinthitis 4.8 320 | lackadaisical 2.66666666667 321 | lambent 4.5 322 | lascivious 2.8 323 | lasciviously 3.22222222222 324 | lax 2.66666666667 325 | laxed 2.66666666667 326 | laxer 2.66666666667 327 | laxity 2.66666666667 328 | laxness 2.66666666667 329 | lecher 2.6 330 | lehman-style 2.7142857142900003 331 | lenient 2.66666666667 332 | lewd 2.8 333 | libertine 2.6 334 | libidinous 1.33333333333 335 | licentious 2.6 336 | licentiousness 2.2 337 | limpid 4.5 338 | limpidity 4.5 339 | limpidly 4.5 340 | loathsome 2.42857142857 341 | longanimity 8.333333333330001 342 | lucious 3.4 343 | luscious 3.4 344 | lusciously 3.4 345 | lush 3.4 346 | lush-looking 3.4 347 | lusher 3.4 348 | lushest 3.4 349 | lushly 3.4 350 | lushness 3.4 351 | lustful 1.33333333333 352 | luxuriant 3.4 353 | maiden 7.4 354 | maidenhood 7.57142857143 355 | maidenly 7.57142857143 356 | manichæans 3.5714285714300003 357 | marring 4.0 358 | martyr 8.57142857143 359 | mediocre 8.0 360 | miserable 1.875 361 | miseries 3.0 362 | miserly 2.85714285714 363 | misery 3.0 364 | mixed-breed 6.5 365 | modesty 8.166666666669999 366 | monogamous 7.625 367 | monogamy 8.0 368 | monogomous 7.625 369 | mud 2.375 370 | muggleborn 7.0 371 | murtad 3.5 372 | nastily 3.22222222222 373 | nauseating 2.25 374 | near-pristine 7.0 375 | no-sex 8.0 376 | noble 7.166666666669999 377 | noesis 8.333333333330001 378 | non-catholic 6.0 379 | non-catholics 6.0 380 | non-celibate 7.625 381 | non-churchgoers 5.42857142857 382 | non-churchgoing 7.4 383 | non-obscene 1.4 384 | non-promiscuous 3.14285714286 385 | non-sacred 8.5 386 | non-sterile 7.8 387 | non-virgin 7.555555555560001 388 | non-virtuous 7.166666666669999 389 | not-so-decent 8.0 390 | not-so-innocent 8.4 391 | nutritious 8.4 392 | nympho 2.75 393 | obscene 1.4 394 | obscenely 5.0 395 | obscenities 2.6 396 | obscenity 2.8 397 | oscillopsia 4.8 398 | outbreak 2.7142857142900003 399 | outrageous 1.4 400 | oversexed 3.14285714286 401 | paint-grade 5.16666666667 402 | pandemic 2.7142857142900003 403 | parishioner 7.4 404 | pas-positive 5.16666666667 405 | passable 8.0 406 | pastor 6.833333333330001 407 | patina 3.3333333333333304 408 | patina-ed 3.3333333333333304 409 | patinate 3.3333333333333304 410 | patinated 3.3333333333333304 411 | patination 3.3333333333333304 412 | pedigreed 6.5 413 | pellucid 4.5 414 | penitent 2.42857142857 415 | penurious 2.85714285714 416 | perserve 6.833333333330001 417 | perv 3.16666666667 418 | perverse 3.25 419 | perversion 1.7142857142857095 420 | perversity 1.7142857142857095 421 | pervert 3.16666666667 422 | perverted 3.25 423 | perverts 1.7142857142857095 424 | pervy 3.16666666667 425 | pestilence 2.7142857142900003 426 | pietistic 7.0 427 | piety 7.42857142857 428 | pimple 4.0 429 | pious 7.0 430 | piously 7.0 431 | piousness 7.42857142857 432 | pitiable 1.875 433 | pitiful 1.875 434 | pitifulness 3.0 435 | poisoned 2.0 436 | polished 6.85714285714 437 | pornographic 2.8 438 | portect 7.75 439 | post-austerity 5.666666666669999 440 | pravity 3.5714285714300003 441 | preist 7.2 442 | preists 7.2 443 | prelate 7.0 444 | preservation 6.833333333330001 445 | preserve 6.833333333330001 446 | preverted 3.16666666667 447 | priest 7.2 448 | pristeen 7.0 449 | pristine 7.0 450 | pristinely 7.0 451 | pristineness 7.0 452 | pro-abstinence 7.714285714289999 453 | profanation 2.75 454 | profanatory 4.0 455 | profane 8.5 456 | profaneness 3.5714285714300003 457 | profanities 2.6 458 | profanity 2.6 459 | profligacy 2.85714285714 460 | profligate 2.85714285714 461 | promiscuity 3.14285714286 462 | promiscuous 3.14285714286 463 | promiscuously 3.14285714286 464 | promiscuousness 3.14285714286 465 | prophane 3.2 466 | prostitute 1.57142857143 467 | prostitutes 3.28571428571 468 | prostitution 3.28571428571 469 | protect 7.75 470 | protected 7.75 471 | protecting 7.75 472 | protestants 6.0 473 | prudery 8.166666666669999 474 | prudishness 8.166666666669999 475 | public-spending 5.666666666669999 476 | pure 9.0 477 | pure-bloods 7.0 478 | pure-breed 6.5 479 | pureblood 7.0 480 | pureblooded 7.0 481 | purebred 6.5 482 | purely 9.0 483 | pureness 7.1111111111100005 484 | purer 7.1111111111100005 485 | purest 9.0 486 | purifying 8.0 487 | purities 7.1111111111100005 488 | purity 7.1111111111100005 489 | rakehell 2.6 490 | rebuke 6.5 491 | reckless 2.6 492 | refined 6.85714285714 493 | refinement 6.85714285714 494 | refining 6.85714285714 495 | refulgent 4.5 496 | religiosity 7.42857142857 497 | religiousness 7.42857142857 498 | repentant 2.42857142857 499 | repenter 2.42857142857 500 | reprobated 3.2 501 | reproof 6.5 502 | reproving 6.5 503 | repugnant 2.42857142857 504 | repulsive 2.42857142857 505 | repulsively 5.0 506 | repulsiveness 2.16666666667 507 | respectable 8.0 508 | reticulin 5.16666666667 509 | retributory 8.333333333330001 510 | revelry 2.2 511 | reverence 8.4 512 | revolting 2.42857142857 513 | revoltingly 5.0 514 | refined 6.85714285714 515 | ruin 2.75 516 | ruination 2.75 517 | ruined 2.75 518 | ruining 2.75 519 | rust-like 3.3333333333333304 520 | ráma 7.4 521 | sacrality 8.4 522 | sacralizing 8.4 523 | sacramentality 8.4 524 | sacred 8.5 525 | sacred- 8.4 526 | sacredness 8.4 527 | sacrilege 2.75 528 | sacrosanct 8.5 529 | sadistic 3.25 530 | saint 8.57142857143 531 | saint-like 8.57142857143 532 | saint-making 8.0 533 | sainthood 8.0 534 | saintlike 8.333333333330001 535 | saintliness 8.14285714286 536 | saintly 7.0 537 | salaciously 3.22222222222 538 | salutary 6.5 539 | sancity 8.4 540 | sanctimonious 7.0 541 | sanctity 8.4 542 | sanitation 5.857142857142861 543 | scam 2.3333333333333304 544 | scammed 2.3333333333333304 545 | scammer 2.3333333333333304 546 | scammers 2.3333333333333304 547 | scamming 2.3333333333333304 548 | schmoly 8.5 549 | sea-green 4.5 550 | self-deceiving 3.0 551 | self-deluded 3.0 552 | self-deluding 3.0 553 | self-delusional 3.0 554 | semi-decent 8.0 555 | semi-innocent 8.4 556 | semi-upright 6.0 557 | sensualist 2.6 558 | sex-for-sale 3.28571428571 559 | sex-trade 3.28571428571 560 | sex-work 3.28571428571 561 | sexily 3.22222222222 562 | sexless 7.625 563 | sick 2.7142857142900003 564 | sickening 2.25 565 | sicker 2.7142857142900003 566 | sickness 2.7142857142900003 567 | sicko 3.16666666667 568 | sin 2.0 569 | sinfulness 2.0 570 | sinner 2.42857142857 571 | sinning 2.0 572 | skank 1.57142857143 573 | skanky 1.33333333333 574 | slattern 1.33333333333 575 | slatternly 1.33333333333 576 | sleazily 3.22222222222 577 | slut 2.75 578 | sluttish 1.33333333333 579 | sluttishly 3.22222222222 580 | slutty 2.75 581 | smarting 3.0 582 | smelliness 2.16666666667 583 | smelly 3.0 584 | soiled 2.6 585 | sophisticated 6.85714285714 586 | sordidly 3.22222222222 587 | spend-thrift 2.85714285714 588 | spendthrift 2.85714285714 589 | splenetic 4.28571428571 590 | spoil 2.75 591 | spotless 7.666666666669999 592 | squalor 2.2 593 | stain 5.16666666667 594 | stain- 5.16666666667 595 | stain-grade 5.16666666667 596 | stainability 5.16666666667 597 | stainable 5.16666666667 598 | stained 2.6 599 | stainer 4.1428571428600005 600 | staining 5.16666666667 601 | stainless 6.166666666669999 602 | stainless-steel 6.166666666669999 603 | stainlesssteel 6.166666666669999 604 | stains 2.6 605 | steel 6.166666666669999 606 | sterile 7.8 607 | sterile-looking 7.8 608 | sterilisation 4.375 609 | sterilise 4.375 610 | sterilised 4.375 611 | sterilising 4.375 612 | sterility 7.8 613 | sterilized 7.8 614 | sterilizing 4.375 615 | sterlization 4.375 616 | sting 3.0 617 | sting-y 3.0 618 | stinger 3.0 619 | stinging 3.0 620 | stomach-turning 2.25 621 | straight-backed 6.0 622 | strict 2.66666666667 623 | stricter 2.66666666667 624 | stringent 2.66666666667 625 | strumpet 1.57142857143 626 | sullied 2.0 627 | sully 3.66666666667 628 | super-clean 7.666666666669999 629 | swain 7.4 630 | swindle 2.3333333333333304 631 | tacky 1.33333333333 632 | taint 2.0 633 | tainted 2.0 634 | tainting 2.0 635 | taints 2.0 636 | tarnish 3.66666666667 637 | tarnished 2.0 638 | tarnishes 3.66666666667 639 | tarnishing 3.66666666667 640 | tasteless 5.33333333333333 641 | tastelessness 5.33333333333333 642 | tawdry 1.33333333333 643 | theologian 7.0 644 | thriftless 2.85714285714 645 | tidier 7.5 646 | tidiness 7.6 647 | tidy 7.666666666669999 648 | tramp 3.0 649 | tramper 3.0 650 | tramping 3.0 651 | trampy 1.33333333333 652 | trashier 1.33333333333 653 | trashiest 1.33333333333 654 | trashiness 1.33333333333 655 | trashy 1.33333333333 656 | trashy-looking 1.33333333333 657 | turbid 4.5 658 | tzniut 8.166666666669999 659 | un-clean 2.76923076923 660 | unabashed 6.0 661 | unadulterated 6.0 662 | unadultered 9.0 663 | unadulturated 9.0 664 | unalloyed 6.0 665 | unappealing 2.42857142857 666 | unblemished 4.0 667 | unbridled 2.6 668 | unchaste 2.66666666667 669 | unchastity 2.66666666667 670 | uncivil 4.28571428571 671 | unclean 2.76923076923 672 | uncleanliness 2.76923076923 673 | uncleanness 2.76923076923 674 | unclouded 4.5 675 | undiluted 9.0 676 | undistilled 6.0 677 | unexceeded 8.333333333330001 678 | ungodly 1.85714285714 679 | unholy 8.5 680 | unimprisoned 8.333333333330001 681 | unmitigated 6.0 682 | unrefined 6.85714285714 683 | unrestrained 2.6 684 | unspoiled 7.0 685 | unstained 5.16666666667 686 | unsterile 7.8 687 | unsullied 7.0 688 | untainted 2.0 689 | unvirtuous 7.166666666669999 690 | unwell 2.7142857142900003 691 | unwholesome 8.4 692 | unworldliness 8.14285714286 693 | upright 6.0 694 | vagabond 3.0 695 | verdant 3.4 696 | verdigris 3.3333333333333304 697 | vertigo 4.8 698 | vile 3.25 699 | vileness 2.16666666667 700 | virgin 7.555555555560001 701 | virginal 7.57142857143 702 | virign 7.555555555560001 703 | virtue 7.42857142857 704 | virtuous 7.166666666669999 705 | virtuously 7.166666666669999 706 | virtuousness 8.14285714286 707 | vitriolic 4.28571428571 708 | vituperative 4.28571428571 709 | vituperous 4.28571428571 710 | vixenish 1.33333333333 711 | voluptuary 2.6 712 | vulgar 2.8 713 | vulgarities 2.6 714 | vulgarity 2.6 715 | wanton 2.6 716 | wantonly 2.6 717 | wantonness 2.6 718 | wasteful 2.85714285714 719 | well-refined 6.85714285714 720 | wholesome 8.4 721 | wholesomely 8.4 722 | wholesomeness 7.1111111111100005 723 | whore 1.57142857143 724 | whorish 1.33333333333 725 | whorishly 3.22222222222 726 | wicked 1.85714285714 727 | wickedest 1.85714285714 728 | wickedly 1.85714285714 729 | wickedness 1.85714285714 730 | wretched 1.875 731 | wretchedly 1.875 732 | wretchedness 3.0 733 | yuckiness 2.16666666667 734 | -------------------------------------------------------------------------------- /moralstrength/data.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import pandas as pd 4 | from glob import glob 5 | from gsitk.preprocess import pprocess_twitter, simple, Preprocessor 6 | from sklearn.pipeline import Pipeline 7 | 8 | ## Read dataset 9 | def read_tw_csv(path, split): 10 | df = pd.read_csv(path, sep='\t', header=None, names=['label', 'text_raw']) 11 | df['split'] = split 12 | return df 13 | 14 | def read_dataset(): 15 | tw_train = read_tw_csv('data/twitter/train/shuffled_dataset_LSTM.csv', 'train') 16 | tw_test = read_tw_csv('data/twitter/test/shuffled_dataset_LSTM.csv', 'test') 17 | dataset = pd.concat([tw_train, tw_test], axis=0).reset_index() 18 | 19 | pp_pipe = Pipeline([ 20 | ('twitter', Preprocessor(pprocess_twitter)), 21 | ('simple', Preprocessor(simple)), 22 | ]) 23 | 24 | dataset['text'] = pp_pipe.fit_transform(dataset['text_raw']) 25 | return dataset 26 | 27 | ## Study 1 dataset 28 | def read_study1_dataset(): 29 | df = pd.read_csv('data/new_dataset/moral_values_and_charitable_donation/study_1/dfProcessed', 30 | sep='\t', error_bad_lines=False) 31 | df = df[df['PostShare']=='post'] 32 | df = df[df['Action']=='True'] 33 | 34 | floats = ['HarmVirtue', 'HarmVice', 'FairnessVirtue', 'FairnessVice','IngroupVirtue', 'IngroupVice', 35 | 'AuthorityVirtue', 'AuthorityVice', 'PurityVirtue', 'PurityVice', 'Morality'] 36 | df[floats] = df[floats].astype(float) 37 | 38 | morality_mean = df['Morality'].mean() 39 | 40 | morals = ['harm', 'fairness', 'ingroup', 'authority', 'purity'] 41 | for moral in morals: 42 | df[moral] = df[[col for col in floats if col.lower().startswith(moral)]].sum(axis=1) 43 | 44 | df['moral'] = df[morals].idxmax(axis=1)[df['Morality'] > morality_mean] 45 | df['moral'] = df['moral'].apply(lambda x: x if x in morals else 'non-moral') 46 | df['moral'] = df['moral'].apply(lambda x: x if x != 'ingroup' else 'loyalty') 47 | df['moral'] = df['moral'].apply(lambda x: x if x != 'harm' else 'care') 48 | df['text_raw'] = df['Tweet'] 49 | df['label'] = df['moral'] 50 | dataset = df[['text_raw', 'label']] 51 | 52 | pp_pipe = Pipeline([ 53 | ('twitter', Preprocessor(pprocess_twitter)), 54 | ('simple', Preprocessor(simple)), 55 | ]) 56 | 57 | dataset['text'] = pp_pipe.fit_transform(dataset['text_raw']) 58 | return dataset 59 | 60 | return df 61 | 62 | ## Read Lexicon 63 | moral_label = {'fairness': 'FC', 'loyalty': 'LB', 'care': 'CH', 'purity': 'PD', 'authority': 'AS', 'non-moral': 'NM'} 64 | 65 | def _read_moral_lex(moral_df, moral_dict, version): 66 | moral_lex = dict() 67 | for moral, df in moral_df.items(): 68 | if version == 'original': 69 | moral_lex[moral] = df.set_index('LEMMA') 70 | else: 71 | moral_lex[moral] = df.set_index('WORD') 72 | moral_lex[moral] = moral_lex[moral].to_dict()['EXPRESSED_MORAL'] 73 | return moral_lex 74 | 75 | def read_moral_lex(version): 76 | 77 | if version == 'original': 78 | folder_path = 'annotations' 79 | else: 80 | folder_path = 'annotations/v{}'.format(version) 81 | 82 | annotations_path = os.path.join(os.path.dirname(__file__), folder_path) 83 | morals = [os.path.splitext(os.path.basename(file))[0] for file in glob(os.path.join(annotations_path, '*.tsv'))] 84 | 85 | moral_df = dict() 86 | moral_dict = dict() 87 | 88 | for moral in morals: 89 | tsv_path = os.path.join(annotations_path, "{}.tsv".format(moral)) 90 | df = pd.read_csv(tsv_path, sep='\t', index_col=False) 91 | moral_df[moral] = df 92 | if version == 'original': 93 | moral_dict[moral] = df['LEMMA'].values 94 | else: 95 | moral_dict[moral] = df.index.values 96 | 97 | moral_lex = _read_moral_lex(moral_df, moral_dict, version) 98 | 99 | return moral_lex 100 | -------------------------------------------------------------------------------- /moralstrength/estimators.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import pickle 4 | from sklearn.pipeline import Pipeline 5 | from gsitk.preprocess import pprocess_twitter, simple, Preprocessor 6 | 7 | from moralstrength.lexicon_use import form_text_vector 8 | 9 | moral_options = ('care', 'fairness', 'loyalty', 'authority', 'purity', 'non-moral') 10 | models = ( 11 | 'simon', 'unigram', 'count', 'freq', 12 | 'simon+count', 'simon+freq', 'simon+count+freq', 13 | 'unigram+count', 'unigram+freq', 'unigram+count+freq', 14 | 'simon+unigram+count', 'simon+unigram+freq', 'simon+unigram+count+freq', 15 | ) 16 | 17 | sorter = {'simon': 0, 'unigram': 1, 'count': 2, 'freq': 3} 18 | 19 | pp_pipe = Pipeline([ 20 | ('twitter', Preprocessor(pprocess_twitter)), 21 | ('simple', Preprocessor(simple)), 22 | ]) 23 | 24 | def generate_model_name(names): 25 | names = sorted(zip(names, [sorter[i] for i in names]), key=lambda x: x[1]) 26 | return '+'.join([n[0] for n in names]) 27 | 28 | def pck_name(model_name, moral, transformer=False, transformer_name='unigram'): 29 | if not transformer: 30 | return "{}_{}_lr".format(model_name, moral) 31 | if transformer_name == 'unigram': 32 | return "ngram" 33 | elif transformer_name == 'simon': 34 | return "simon_{}".format(moral) 35 | 36 | def load_model(model_name): 37 | dir_path = os.path.dirname(os.path.realpath(__file__)) 38 | model_path = 'export/{}.pck'.format(model_name) 39 | full_path = os.path.join(dir_path, model_path) 40 | with open(full_path, 'rb') as f: 41 | m = pickle.load(f) 42 | return m 43 | 44 | def load_models(estimators, transformers, moral): 45 | model_name = generate_model_name(estimators) 46 | lr = load_model(pck_name(model_name, moral)) 47 | 48 | trans = {} 49 | for transformer in transformers: 50 | if transformer == 'unigram' or transformer == 'simon': 51 | name_tmp = pck_name( 52 | transformer, moral, 53 | transformer=True, transformer_name=transformer 54 | ) 55 | trans[transformer] = load_model(name_tmp) 56 | else: 57 | trans[transformer] = None 58 | 59 | return lr, trans 60 | 61 | def get_estimators(estimator_name): 62 | return estimator_name.split('+') 63 | 64 | def select_processes(process_names, moral): 65 | estimators = get_estimators(process_names) 66 | transformers = [] 67 | for estimator in estimators: 68 | transformers.append(estimator) 69 | #if estimator == 'simon': 70 | # transformers.append('simon') 71 | #elif estimator == 'unigram': 72 | # transformers.append('unigram') 73 | return estimators, transformers 74 | 75 | 76 | 77 | def estimate(text, moral, model='count', from_file=False): 78 | if not from_file: 79 | text_processed = pp_pipe.transform([text]) 80 | else: 81 | with open('lines.txt', 'r') as f: 82 | lines = f.readlines() 83 | lines = [line.strip() for line in lines] 84 | text_processed = pp_pipe.transform(lines) 85 | 86 | estimators, transformers = select_processes(model, moral) 87 | lr, transformers = load_models(estimators, transformers, moral) 88 | 89 | X = [] 90 | for transformer in transformers.keys(): 91 | if transformer == 'unigram': 92 | X_tmp = transformers[transformer].transform([' '.join(t) for t in text_processed]) 93 | X_tmp = X_tmp.toarray() 94 | X.append(X_tmp) 95 | elif transformer == 'simon': 96 | X_tmp = transformers[transformer].transform(text_processed) 97 | X.append(X_tmp) 98 | else: 99 | X_tmp = [form_text_vector(t, model=transformer) for t in text_processed] 100 | X.append(X_tmp) 101 | X = np.hstack(X) 102 | 103 | return lr.predict_proba(X)[:, 1] 104 | -------------------------------------------------------------------------------- /moralstrength/export/count_authority_lr.pck: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /moralstrength/lexicon_use.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | from moralstrength import data 4 | from moralstrength.moral_list import moral_options_lexicon 5 | 6 | moral_lex = data.read_moral_lex('original') 7 | new_moral_lex = data.read_moral_lex('1.1') 8 | current_moral_lex = new_moral_lex 9 | 10 | def select_version(version): 11 | global current_moral_lex 12 | if version == 'original': 13 | current_moral_lex = moral_lex 14 | elif version == 'latest': 15 | current_moral_lex = new_moral_lex 16 | 17 | def moral_value(word, moral, normalized=False): 18 | value = __private_moral_value(word, moral) 19 | if value==-1: 20 | return float('NaN') 21 | #annotation is between 1 and 9 22 | if normalized: 23 | value = (value-1)/8 24 | return value 25 | 26 | def __private_moral_value(word, moral): 27 | v = current_moral_lex[moral].get(word, -1) 28 | if isinstance(v, pd.Series): 29 | return v.values[0] 30 | return v 31 | 32 | def bucketize(x): 33 | if x == -1 : 34 | return np.array([0, 0]) 35 | if x > 5: 36 | return np.array([0, 1]) 37 | elif x <= 5: 38 | return np.array([1, 0]) 39 | 40 | def form_word_vector(word, bucketize_=None): 41 | v = [] 42 | for moral in moral_options_lexicon: 43 | v_m = __private_moral_value(word, moral) 44 | if bucketize_ is not None: 45 | v_m = bucketize_(v_m) 46 | v.append(v_m) 47 | 48 | if bucketize_ is not None: 49 | return np.concatenate(v) 50 | 51 | v = np.array(v) 52 | return v 53 | 54 | def form_text_vector(text, model='count'): 55 | ''' 56 | model: count of freq 57 | ''' 58 | if model == 'count': 59 | bucketize_ = bucketize 60 | elif model == 'freq': 61 | bucketize_ = None 62 | 63 | v = [] 64 | for word in text: 65 | v.append(form_word_vector(word, bucketize_=bucketize_)) 66 | if model == 'count': 67 | return np.sum(v, axis=0) 68 | elif model == 'freq': 69 | return np.concatenate( 70 | ( 71 | np.average(v, axis=0), 72 | np.std(v, axis=0), 73 | np.median(v, axis=0), 74 | np.max(v, axis=0), 75 | )) 76 | 77 | -------------------------------------------------------------------------------- /moralstrength/lines.txt: -------------------------------------------------------------------------------- 1 | My cat is happy 2 | I really care for my cat 3 | She hates going to the movies 4 | PLS help #HASHTAG's family. No one prepares for this. They are in need of any assistance you can offer 5 | -------------------------------------------------------------------------------- /moralstrength/moral_list.py: -------------------------------------------------------------------------------- 1 | moral_options_lexicon = ['care', 'fairness', 'loyalty', 'authority', 'purity'] 2 | moral_options_predictions = moral_options_lexicon + ['non-moral'] 3 | -------------------------------------------------------------------------------- /moralstrength/moralstrength.py: -------------------------------------------------------------------------------- 1 | from moralstrength import lexicon_use 2 | from moralstrength.estimators import estimate,models 3 | from moralstrength.moral_list import moral_options_lexicon, moral_options_predictions 4 | import math 5 | import numpy as np 6 | import pandas as pd 7 | import spacy 8 | 9 | try: 10 | nlp = spacy.load("en_core_web_sm") 11 | nlp_reduced = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner"]) 12 | except OSError as error: 13 | if "Can't find model 'en_core_web_sm'" in error.args[0]: 14 | print('Downloading files required by the Spacy language processing library (this is only required once)') 15 | spacy.cli.download('en_core_web_sm') 16 | nlp = spacy.load("en_core_web_sm") 17 | nlp_reduced = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner"]) 18 | 19 | 20 | def word_moral_value(word, moral, normalized=False): 21 | """Returns the association strength between word and moral trait, 22 | as rated by annotators. Value ranges from 1 to 9. 23 | 1: words closely associated to vices: harm, cheating, betrayal, subversion, degradation 24 | 9: words closely associated to virtues: care, fairness, loyalty, authority, sanctity 25 | normalized=True will normalize each annotation in the range -1 (vice) to +1 (virtue) 26 | If the word is not in the lexicon of that moral trait, returns NaN. 27 | For a list of available traits, get_available_lexicon_traits() 28 | """ 29 | 30 | if moral not in moral_options_lexicon: 31 | raise ValueError('Invalid moral trait "{}" specified. Valid traits are: {}'.format(moral,moral_options_lexicon)) 32 | return lexicon_use.moral_value(word=word, moral=moral, normalized=normalized) 33 | 34 | 35 | def word_moral_annotations(word, normalized=False): 36 | """Returns a dict that gives the association strength between word and every 37 | moral trait, as rated by annotators. Value ranges from 1 to 9. 38 | 1: words closely associated to harm, cheating, betrayal, subversion, degradation 39 | 9: words closely associated to care, fairness, loyalty, authority, purity/sanctity 40 | normalized=True will normalize each annotation in the range -1 (vice) to +1 (virtue) 41 | If the word is not in the lexicon of that moral trait, returns NaN.""" 42 | 43 | return {moral: lexicon_use.moral_value(word=word, moral=moral, normalized=normalized) for moral in moral_options_lexicon} 44 | 45 | 46 | def string_average_moral(text,moral): 47 | """Returns the average of the annotations for the words in the sentence (for one moral). 48 | If no word is recognized/found in the lexicon, returns -1. 49 | Words are lemmatized using spacy. 50 | """ 51 | 52 | # TODO: add new metric 53 | 54 | sum = 0 55 | recognized_words_no = 0 56 | for token in nlp(text): 57 | lemma = token.lemma_ 58 | value = word_moral_value(lemma,moral) 59 | #print("lemma: {}".format(lemma)) 60 | if value>-1: 61 | sum += value 62 | recognized_words_no += 1 63 | if recognized_words_no == 0: 64 | return float('NaN') 65 | else: 66 | return sum/recognized_words_no 67 | 68 | 69 | def string_vader_moral(text,moral,alpha=15): 70 | """Returns a normalized annotation score for the words in the sentence (for one moral). 71 | Score ranges from -1 (vices) to 1 (virtues) and is calculated as in VADER (Hutto and Gilbert, 2014). 72 | The optional alpha parameter defines how quickly the score maxes out (to 1 or -1), 73 | and can be tweaked (e.g. for very long texts it *might* be more sensible to set it to 150; 15 is the default taken from VADER). 74 | If no word is recognized/found in the lexicon, returns NaN. 75 | Text is lemmatized using spacy. 76 | """ 77 | score = 0 78 | recognized_words_no = 0 79 | for token in nlp(text): 80 | lemma = token.lemma_ 81 | value = word_moral_value(lemma,moral,normalized=True) 82 | if not math.isnan(value): 83 | score += value 84 | recognized_words_no += 1 85 | if recognized_words_no == 0: 86 | return float('NaN') 87 | else: 88 | return score/math.sqrt((score*score) + alpha) 89 | 90 | def texts_moral(texts, moral, process=False): 91 | """Estimates the moral value of the selected moral trait 92 | for a list of documents. The list could contain only one 93 | document, or many. The process parameter (by default False) 94 | controls whether each document is processed with spacy, 95 | performing the lemmatization. If no moral value is found, 96 | the document is estimated a NaN value. Estimation is done 97 | using the lexicon annotations, with no learning whatsoever. 98 | """ 99 | Sums = [] 100 | if process: 101 | docs = list(nlp_reduced.pipe(texts)) 102 | else: 103 | docs = texts 104 | for doc in docs: 105 | result = None 106 | summ = 0 107 | recognized_words_no = 0 108 | for token in doc: 109 | lemma = token.lemma_ 110 | value = word_moral_value(lemma, moral) 111 | if value>-1: 112 | summ += value 113 | recognized_words_no += 1 114 | if recognized_words_no == 0: 115 | result = float('NaN') 116 | else: 117 | result = summ/recognized_words_no 118 | Sums.append(result) 119 | 120 | return Sums 121 | 122 | def texts_morals(texts, process=False): 123 | """An useful wrapper for the texts_moral function. Estimates 124 | all available moral values for a list of text. Returns a numpy 125 | array with 2 dimensions (number of documents x number of moral traits). 126 | The order of moral traits is found by executing the method lexicon_morals. 127 | """ 128 | S = [] 129 | if process: 130 | docs = list(nlp_reduced.pipe(texts)) 131 | else: 132 | docs = texts 133 | for moral in lexicon_morals(): 134 | sums = texts_moral(docs, moral, process=False) 135 | S.append(sums) 136 | return np.array(S).T 137 | 138 | def estimate_morals(texts, process=False): 139 | """Wrapper of the texts_morals function. It returns the moral estimation in a 140 | Pandas DataFrame, being the columns the morals, and the rows the different documents. 141 | This should be the method used analyzing text without using any machine learning. It uses 142 | only the annotations of the lexicon. 143 | """ 144 | estimation = texts_morals(texts, process=process) 145 | estimation = pd.DataFrame(columns=lexicon_morals(), data=estimation) 146 | return estimation 147 | 148 | 149 | def get_available_models(): 150 | """Returns a list of available models for predicting texts. 151 | Short explanation of names: 152 | unigram: simple unigram-based model 153 | count: number of words that are rated as closer to moral extremes 154 | freq: distribution of moral ratings across the text 155 | simon: SIMilarity-based sentiment projectiON 156 | or a combination of these. 157 | For a comprehensive explanation of what each model does and how it performs on 158 | different datasets, see https://arxiv.org/abs/1904.08314 159 | (published at Knowledge-Based Systems).""" 160 | 161 | return models 162 | 163 | def get_available_lexicon_traits(): 164 | """Returns a list of traits that were annotated and can be queried 165 | by word_moral_value(). 166 | care: Care/Harm 167 | fairness: Fairness/Cheating 168 | loyalty: Loyalty/Betrayal 169 | authority: Authority/Subversion 170 | purity: Purity/Degradation 171 | """ 172 | 173 | return moral_options_lexicon 174 | 175 | def lexicon_morals(): 176 | """Syntactic sugar call to get_available_lexicon_traits. 177 | """ 178 | 179 | return get_available_lexicon_traits() 180 | 181 | 182 | def get_available_prediction_traits(): 183 | """Returns a list of traits that can be predicted by string_moral_value() 184 | or file_moral_value(). 185 | care: Care/Harm 186 | fairness: Fairness/Cheating 187 | loyalty: Loyalty/Betrayal 188 | authority: Authority/Subversion 189 | purity: Purity/Degradation 190 | non-moral: Tweet/text is non-moral 191 | """ 192 | 193 | return moral_options_predictions 194 | 195 | def string_moral_value(text,moral,model='unigram+freq'): 196 | """Returns the estimated probability that the text is relevant to either a vice or 197 | virtue of the corresponding moral trait. 198 | The default model is unigram+freq, the best performing (on average) across all 199 | dataset, according to our work. 200 | For a list of available models, see get_available_models(). 201 | For a list of traits, get_available_prediction_traits(). 202 | """ 203 | 204 | if model not in models: 205 | raise ValueError('Invalid model "{}" specified. Valid models are: {}'.format(model,models)) 206 | if moral not in moral_options_predictions: 207 | raise ValueError('Invalid moral trait "{}" specified. Valid traits are: {}'.format(moral,moral_options_predictions)) 208 | return estimate(text, moral=moral, model=model)[0] 209 | 210 | def string_moral_values(text,model='unigram+freq'): 211 | """Returns the estimated probability that the text is relevant to vices or virtues 212 | of all moral traits, as a dict. 213 | The default model is unigram+freq, the best performing (on average) across all 214 | dataset, according to our work. 215 | For a list of available models, see get_available_models(). 216 | For a list of traits, get_available_prediction_traits(). 217 | """ 218 | 219 | if model not in models: 220 | raise ValueError('Invalid model "{}" specified. Valid models are: {}'.format(model,models)) 221 | return {moral: estimate(text=text, moral=moral, model=model)[0] for moral in moral_options_predictions} 222 | 223 | # def file_moral_value(filename,trait,model): 224 | # if model not in models: 225 | # raise ValueError('Invalid model "{}" specified. Valid models are: {}'.format(model,models)) 226 | # if moral not in moral_options_predictions: 227 | # raise ValueError('Invalid moral trait "{}" specified. Valid traits are: {}'.format(moral,moral_options_predictions)) 228 | # 229 | # return 230 | # 231 | # def file_moral_values(filename,model): 232 | # if model not in models: 233 | # raise ValueError('Invalid model "{}" specified. Valid models are: {}'.format(model,models)) 234 | # return 235 | -------------------------------------------------------------------------------- /moralstrength/moralstrengthdict.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import data 4 | 5 | moral_df, moral_dict = data.read_moral_lex() 6 | 7 | moral_lex = dict() 8 | for moral, df in moral_df.items(): 9 | moral_lex[moral] = df.set_index('LEMMA') 10 | 11 | moral_list = list(moral_lex.keys()) 12 | 13 | def moral_value(word, moral): 14 | try: 15 | v = moral_lex[moral].loc[word]['EXPRESSED_MORAL'] 16 | except KeyError: 17 | v = -1 18 | if isinstance(v, pd.Series): 19 | return v.values[0] 20 | return v 21 | 22 | def bucketize(x): 23 | if x == -1 : 24 | return np.array([0, 0]) 25 | if x > 5: 26 | return np.array([0, 1]) 27 | elif x <= 5: 28 | return np.array([1, 0]) 29 | 30 | def form_word_vector(word, bucketize_=None): 31 | v = [] 32 | for moral in moral_lex.keys(): 33 | v_m = moral_value(word, moral) 34 | if bucketize_ is not None: 35 | v_m = bucketize_(v_m) 36 | v.append(v_m) 37 | 38 | if bucketize_ is not None: 39 | return np.concatenate(v) 40 | 41 | v = np.array(v) 42 | return v 43 | 44 | def form_text_vector(text, model='count'): 45 | ''' 46 | model: count of freq 47 | ''' 48 | if model == 'count': 49 | bucketize_ = bucketize 50 | elif model == 'freq': 51 | bucketize_ = None 52 | 53 | v = [] 54 | for word in text: 55 | v.append(form_word_vector(word, bucketize_=bucketize_)) 56 | if model == 'count': 57 | return np.sum(v, axis=0) 58 | elif model == 'freq': 59 | return np.concatenate( 60 | ( 61 | np.average(v, axis=0), 62 | np.std(v, axis=0), 63 | np.median(v, axis=0), 64 | np.max(v, axis=0), 65 | )) 66 | 67 | -------------------------------------------------------------------------------- /moralstrength_annotations/authority.tsv: -------------------------------------------------------------------------------- 1 | LEMMA EXPRESSED_MORAL 2 | abide 4.16666666667 3 | agitation 2.8 4 | alienate 4.28571428571 5 | apostasy 4.0 6 | apostate 4.16666666667 7 | authoritarian 8.2 8 | authoritative 7.833333333330001 9 | authority 8.8 10 | betrayal 3.14285714286 11 | bourgeoisie 6.57142857143 12 | caste 6.75 13 | casteless 4.5 14 | class 6.875 15 | command 7.285714285710001 16 | compliance 6.8 17 | compliant 6.0 18 | comply 6.833333333330001 19 | control 8.0 20 | defect 4.33333333333 21 | defector 2.2 22 | defer 4.57142857143 23 | deferential 4.42857142857 24 | defiance 2.83333333333 25 | defiant 3.5 26 | defy 2.83333333333 27 | denounce 4.0 28 | deserted 3.8 29 | disloyal 4.55555555556 30 | disloyalty 2.2857142857099997 31 | disobedience 3.5 32 | disobey 3.16666666667 33 | disrespect 3.66666666667 34 | dissension 2.4 35 | dissent 3.83333333333 36 | dissentient 4.0 37 | dissident 2.0 38 | dutiable 6.4 39 | duty 7.166666666669999 40 | father 7.666666666669999 41 | fatherhood 6.0 42 | fatherless 4.66666666667 43 | fatherlike 4.6 44 | fatherliness 5.833333333330001 45 | heretic 4.5 46 | hierarchical 7.714285714289999 47 | hierarchy 6.42857142857 48 | honor 6.0 49 | honorarium 5.25 50 | honorary 7.2 51 | honoree 6.333333333330001 52 | honorific 6.25 53 | illegal 3.2 54 | illegalise 5.1428571428600005 55 | insubordination 5.16666666667 56 | insurgent 2.55555555556 57 | law 8.666666666669999 58 | lawless 2.16666666667 59 | leader 7.1428571428600005 60 | leadership 8.8 61 | legal 7.8 62 | legalisation 7.166666666669999 63 | loyal 5.125 64 | loyalist 7.8 65 | loyalty 5.4 66 | mother 6.5 67 | motherhood 6.333333333330001 68 | motherland 6.333333333330001 69 | motherless 3.66666666667 70 | motherlike 5.375 71 | motherliness 5.833333333330001 72 | mutinous 2.625 73 | nonconformist 3.4 74 | obedience 5.28571428571 75 | obey 4.4 76 | obstruct 4.0 77 | oppose 2.5 78 | order 8.0 79 | orderliness 7.0 80 | permission 6.5 81 | permit 6.166666666669999 82 | position 7.6 83 | preserve 5.0 84 | protest 2.8 85 | rank 7.2 86 | ranker 5.0 87 | rankin 5.0 88 | rebel 3.0 89 | rebellion 3.16666666667 90 | rebellious 2.7142857142900003 91 | refuse 3.85714285714 92 | remonstrate 5.0 93 | respect 5.8 94 | revere 7.0 95 | reverential 5.57142857143 96 | riot 2.3 97 | rioter 2.1428571428599996 98 | riotous 3.0 99 | sedition 3.0 100 | serve 4.8 101 | status 5.16666666667 102 | submission 6.0 103 | submit 4.75 104 | submitter 4.375 105 | subversion 1.2 106 | subvert 3.0 107 | supremacy 6.833333333330001 108 | traditional 5.8 109 | traditionalist 5.88888888889 110 | traitor 1.75 111 | treacherous 3.6 112 | unfaithful 2.0 113 | veneration 6.666666666669999 114 | obedience 7.0 115 | offence 3.8 116 | forbid 4.16666666666667 117 | garrison 4.75 118 | mildly 4.8 119 | mandate 7.142857142857139 120 | rigidly 4.6 121 | exemplary 6.4 122 | perilous 4.5 123 | anxiety 6.0 124 | patience 5.75 125 | polite 7.6 126 | belief 5.0 127 | cherish 3.6 128 | devoted 4.2 129 | indifference 3.0 130 | revolt 2.0 131 | classify 6.0 132 | -------------------------------------------------------------------------------- /moralstrength_annotations/care.tsv: -------------------------------------------------------------------------------- 1 | LEMMA EXPRESSED_MORAL 2 | abandon 1.6 3 | abuse 2.5 4 | amity 5.42857142857 5 | annihilation 1.33333333333 6 | attack 2.66666666667 7 | benefit 7.0 8 | brutal 2.33333333333 9 | brutalise 1.2 10 | care 8.8 11 | compassion 7.0 12 | compassionate 8.166666666669999 13 | cruelly 1.6 14 | cruelty 3.4 15 | crush 2.4 16 | crusher 2.8 17 | damage 3.0 18 | defendant 3.2 19 | defenestration 3.83333333333 20 | defense 5.0 21 | defenseless 4.4 22 | destroy 1.0 23 | detrimental 2.4 24 | empathetic 6.8 25 | empathy 8.5 26 | endangered 4.6 27 | endangerment 1.8 28 | exploit 2.83333333333 29 | fight 4.0 30 | guard 6.8 31 | hurt 2.2857142857099997 32 | impair 2.6 33 | kill 1.6 34 | killer 1.0 35 | peace 7.8 36 | peaceable 7.8571428571399995 37 | peacekeeping 7.8 38 | peacemaker 8.6 39 | peacenik 6.4 40 | peacetime 7.8 41 | preserve 7.6 42 | protection 8.0 43 | protectionism 6.8 44 | protectionist 8.2 45 | protector 8.57142857143 46 | protectorship 8.8 47 | ravage 3.4 48 | ruin 1.4 49 | safe 8.6 50 | safeguard 6.0 51 | safehold 6.6 52 | safekeeping 8.8 53 | safety 8.2 54 | security 8.2 55 | shelter 7.0 56 | shield 7.5 57 | spurn 3.0 58 | stomp 2.33333333333 59 | suffer 2.6 60 | sympathetic 8.333333333330001 61 | sympathize 8.0 62 | sympathy 8.57142857143 63 | violence 1.6666666666699999 64 | violent 2.83333333333 65 | war 1.6666666666699999 66 | wound 2.2 67 | disgusted 2.8 68 | abuse 1.7142857142857097 69 | quarrel 1.8333333333333302 70 | healthcare 8.6 71 | opponent 3.2 72 | refuge 6.75 73 | dignity 7.0 74 | assassin 1.0 75 | collateral 3.8333333333333304 76 | destruction 1.14285714285714 77 | bang 1.0 78 | preserving 7.4 79 | annihilation 1.16666666666667 80 | broken 2.1666666666666696 81 | intrusive 2.6 82 | wrecked 1.75 83 | critically 3.2 84 | upsetting 3.8333333333333304 85 | -------------------------------------------------------------------------------- /moralstrength_annotations/fairness.tsv: -------------------------------------------------------------------------------- 1 | LEMMA EXPRESSED_MORAL 2 | balance 5.6 3 | bias 5.2 4 | bigot 3.6 5 | bigotry 4.0 6 | constant 4.5 7 | discrimination 4.0 8 | discriminatory 4.0 9 | dishonest 5.2 10 | disproportion 3.16666666667 11 | disproportionate 3.6 12 | dissociate 3.83333333333 13 | egalitarian 6.0 14 | equable 6.2 15 | equal 7.0 16 | equaliser 6.2 17 | equalitarian 5.33333333333 18 | equity 5.125 19 | equivalent 5.0 20 | evenness 5.16666666667 21 | exclude 4.2 22 | exclusion 3.6 23 | fair 9.0 24 | fair-minded 7.166666666669999 25 | fair-mindedness 8.71428571429 26 | homologous 2.5 27 | honest 7.2 28 | honesty 7.666666666669999 29 | impartial 8.0 30 | injustice 6.5 31 | justice 7.6 32 | justification 6.1428571428600005 33 | justificatory 5.5 34 | justify 5.71428571429 35 | preference 5.4 36 | prejudgment 4.125 37 | prejudice 3.83333333333 38 | prejudicial 6.0 39 | reasonable 5.0 40 | reciprocal 6.4 41 | right 8.166666666669999 42 | segregation 5.75 43 | segregationism 2.2 44 | segregationist 2.8 45 | tolerant 7.166666666669999 46 | unbiased 8.6 47 | unequal 4.5 48 | unequalised 4.0 49 | unequalled 3.66666666667 50 | unfair 4.625 51 | unjust 6.333333333330001 52 | unjustified 4.625 53 | unprejudiced 6.71428571429 54 | unscrupulous 2.7142857142900003 55 | slavery 3.6666666666666696 56 | impartial 7.0 57 | permanency 5.16666666666667 58 | wrongfully 5.4 59 | homophobic 4.0 60 | inhumane 4.66666666666667 61 | bigotry 4.33333333333333 62 | inaccurate 4.4 63 | subjective 5.5 64 | competent 6.4 65 | consumerism 5.5 66 | amplitude 5.16666666666667 67 | honest 7.55555555555556 68 | disproportionately 3.28571428571429 69 | accepting 7.83333333333333 70 | relatively 4.875 71 | oppression 5.75 72 | proportionality 3.0 73 | -------------------------------------------------------------------------------- /moralstrength_annotations/loyalty.tsv: -------------------------------------------------------------------------------- 1 | LEMMA EXPRESSED_MORAL 2 | abandon 1.42857142857 3 | ally 8.71428571429 4 | apostasy 3.8 5 | apostate 4.2 6 | betrayal 1.0 7 | cadre 6.0 8 | clique 5.88888888889 9 | cliquish 5.4 10 | cohort 6.0 11 | collective 7.333333333330001 12 | collectivism 7.1111111111100005 13 | collectivist 6.4 14 | communal 6.2 15 | commune 5.375 16 | communism 4.42857142857 17 | communist 3.85714285714 18 | community 6.2 19 | comradeship 7.57142857143 20 | deceit 1.125 21 | deceive 1.33333333333 22 | deserted 2.77777777778 23 | devoted 8.0 24 | devotedness 8.0 25 | devotee 7.2 26 | disloyal 1.8571428571400002 27 | disloyalty 1.8333333333300001 28 | family 8.0 29 | fellow 7.1428571428600005 30 | fellowship 8.166666666669999 31 | foreign 4.5 32 | group 6.375 33 | guild 7.1428571428600005 34 | homeland 7.1428571428600005 35 | immigration 4.83333333333 36 | imposter 2.2857142857099997 37 | individual 4.28571428571 38 | individualised 4.0 39 | individualistic 3.8 40 | insider 5.625 41 | jilted 2.375 42 | joint 6.833333333330001 43 | loyal 8.875 44 | loyalist 7.555555555560001 45 | loyalty 7.555555555560001 46 | member 6.625 47 | miscreant 2.8 48 | national 5.2 49 | nationalisation 5.6 50 | nationalist 6.0 51 | nationhood 7.0 52 | nationwide 6.0 53 | patriot 7.8571428571399995 54 | renegade 4.16666666667 55 | segregation 4.75 56 | segregationism 3.14285714286 57 | segregationist 4.5 58 | sequester 4.71428571429 59 | solidarity 8.6 60 | spy 2.875 61 | terrorise 1.6 62 | terrorism 3.85714285714 63 | terrorist 2.66666666667 64 | together 7.2 65 | traitor 1.8 66 | treacherous 1.57142857143 67 | unison 7.5 68 | united 8.5 69 | allegiance 8.9 70 | dedication 6.4 71 | founder 6.375 72 | lust 4.42857142857143 73 | siding 7.5 74 | amity 7.857142857142861 75 | cowardice 3.25 76 | loyalist 8.571428571428571 77 | bandit 1.5 78 | nationalism 6.83333333333333 79 | consulate 6.33333333333333 80 | rogue 2.6666666666666696 81 | insulting 3.5 82 | cruelty 3.3333333333333304 83 | clap 7.5 84 | lovingly 7.0 85 | -------------------------------------------------------------------------------- /moralstrength_annotations/purity.tsv: -------------------------------------------------------------------------------- 1 | LEMMA EXPRESSED_MORAL 2 | abstinence 7.714285714289999 3 | adulterated 2.83333333333 4 | adulterine 4.11111111111 5 | adultery 1.5 6 | apostasy 3.2 7 | apostate 3.5 8 | austerity 5.666666666669999 9 | blemish 4.0 10 | celibacy 8.0 11 | celibate 7.625 12 | chaste 7.57142857143 13 | chastening 6.5 14 | church 6.833333333330001 15 | churchgoer 7.4 16 | churchgoing 5.42857142857 17 | churchman 7.0 18 | clean 7.666666666669999 19 | cleaner 7.5 20 | cleanliness 7.6 21 | cleansing 8.0 22 | cleanup 6.5 23 | contagion 2.7142857142900003 24 | contagious 3.5 25 | debauchee 2.6 26 | debauchery 2.2 27 | decent 8.0 28 | decentalisation 5.0 29 | depraved 3.25 30 | desecration 2.75 31 | dirt 2.375 32 | dirtily 3.22222222222 33 | dirty 3.0 34 | disease 3.0 35 | disgustingness 2.16666666667 36 | exploit 3.16666666667 37 | exploitatory 2.7142857142900003 38 | filth 2.2 39 | filthily 5.0 40 | filthy 2.0 41 | gross 2.625 42 | heretic 2.5714285714300003 43 | holy 8.5 44 | impiety 3.5714285714300003 45 | impious 3.2 46 | indecent 2.8 47 | innocent 8.4 48 | integrity 7.8 49 | intemperate 4.28571428571 50 | lax 2.66666666667 51 | limpid 4.5 52 | maiden 7.4 53 | modesty 8.166666666669999 54 | obscene 1.4 55 | pervert 3.16666666667 56 | piety 7.42857142857 57 | pious 7.0 58 | preserve 6.833333333330001 59 | pristine 7.0 60 | profanatory 4.0 61 | profanity 2.6 62 | profligate 2.85714285714 63 | promiscuous 3.14285714286 64 | prostitution 3.2857142857099997 65 | pure 9.0 66 | pureblood 7.0 67 | purebred 6.5 68 | purity 7.1111111111100005 69 | refined 6.8571428571399995 70 | repulsive 2.4285714285699997 71 | ruin 2.75 72 | sacred 8.5 73 | sacredness 8.4 74 | saint 8.57142857143 75 | sainthood 8.0 76 | saintlike 8.333333333330001 77 | saintliness 8.14285714286 78 | sick 2.7142857142900003 79 | sickening 2.25 80 | sin 2.0 81 | sinner 2.4285714285699997 82 | slut 2.75 83 | sluttish 1.33333333333 84 | stainable 5.16666666667 85 | stained 2.6 86 | stainer 4.1428571428600005 87 | stainless 6.166666666669999 88 | sterile 7.8 89 | sterilisation 4.375 90 | tainted 2.0 91 | tarnish 3.66666666667 92 | tramp 3.0 93 | trashy 1.33333333333 94 | unadulterated 6.0 95 | unchaste 2.66666666667 96 | uncleanliness 2.76923076923 97 | upright 6.0 98 | virgin 7.555555555560001 99 | virtuous 7.166666666669999 100 | wanton 2.6 101 | wholesome 8.4 102 | whore 1.57142857143 103 | wicked 1.8571428571400002 104 | wretched 1.875 105 | wretchedness 3.0 106 | protect 7.75 107 | lush 3.4 108 | vertigo 4.8 109 | freshener 7.83333333333333 110 | deluded 3.0 111 | priest 7.2 112 | tasteless 5.33333333333333 113 | saint 7.0 114 | scam 2.3333333333333304 115 | patina 3.3333333333333304 116 | abstinence 4.5 117 | sting 3.0 118 | hygiene 5.857142857142861 119 | prostitution 1.6666666666666698 120 | perversion 1.7142857142857097 121 | Catholics 6.0 122 | -------------------------------------------------------------------------------- /moralstrength_raw/RAW_ANNOTATIONS/Readme.txt: -------------------------------------------------------------------------------- 1 | This folder contains the raw annotations collected from figure-eight. 2 | 3 | The folder all_annotators_except_failed contains all the annotations collected, except for the annotators that failed the task (see the paper for details on the control questions, which were based on valence ratings from Warriner et al.). 4 | 5 | The folder filtered_annotators contains the annotations after the annotators with low inter-annotator agreement were removed. 6 | 7 | The filename is RAW_ANNOTATIONS_[MORAL], where MORAL is the moral trait considered and can either be AUTHORITY, CARE, FAIRNESS, LOYALTY or PURITY. 8 | 9 | The fields in each file are: 10 | WORD the word to be annotated 11 | ANNOTATOR_ID the unique ID of each annotator 12 | VALENCE the valence rating of WORD, on a scale from 1 (low) to 9 (high) 13 | AROUSAL the arousal rating of WORD, on a scale from 1 (low) to 9 (high) 14 | RELEVANCE whether WORD is related to the MORAL 15 | EXPRESSED_MORAL the moral strength of WORD, i.e. whether it is closer to one or the other extremes pertaining the MORAL trait. 16 | 17 | The numbers for EXPRESSED_MORAL range from 1 to 9, and the extremes of the scales are: 18 | 1=Subversion, 9=Authority for AUTHORITY 19 | 1=Harm, 9=Care for CARE 20 | 1=Proportionality, 9=Fairness for FAIRNESS 21 | 1=Disloyalty, 9=Loyalty for LOYALTY 22 | 1=Degradation, 9=Purity for PURITY 23 | 24 | For privacy reason, the annotator ID has been salted and hashed, so that going back to the original annotator ID is not possible, but it is still possible to track each annotator's ratings across the different morals. 25 | -------------------------------------------------------------------------------- /moralstrength_raw/RAW_ANNOTATIONS/all_annotators_except_failed/RAW_ANNOTATIONS_FAIRNESS.txt: -------------------------------------------------------------------------------- 1 | WORD ANNOTATOR_ID RELEVANCE VALENCE AROUSAL EXPRESSED_MORAL 2 | competent D6180738F37A31312E820B069C47C3C0 2 7 7 3 3 | wrongfully D6180738F37A31312E820B069C47C3C0 1 2 3 4 4 | slavery D6180738F37A31312E820B069C47C3C0 1 5 2 5 5 | disproportionately D6180738F37A31312E820B069C47C3C0 1 5 5 4 6 | impartial D6180738F37A31312E820B069C47C3C0 1 7 6 9 7 | confounding D6180738F37A31312E820B069C47C3C0 2 4 5 5 8 | bigotry D6180738F37A31312E820B069C47C3C0 1 6 2 4 9 | relatively D6180738F37A31312E820B069C47C3C0 1 6 6 5 10 | inhumane D6180738F37A31312E820B069C47C3C0 1 2 7 2 11 | proportionality D6180738F37A31312E820B069C47C3C0 1 5 2 1 12 | consumerism D6180738F37A31312E820B069C47C3C0 1 6 6 4 13 | accepting D6180738F37A31312E820B069C47C3C0 1 4 6 7 14 | permanency 8F19CDD26BDBD0605677A832E6EF7B80 1 5 5 7 15 | honest 8F19CDD26BDBD0605677A832E6EF7B80 1 9 1 9 16 | proportionality 8F19CDD26BDBD0605677A832E6EF7B80 2 5 4 4 17 | impartial E6F53B54A4814DA2614922482CD00894 1 8 5 9 18 | homophobic E6F53B54A4814DA2614922482CD00894 2 1 2 1 19 | permanency E6F53B54A4814DA2614922482CD00894 1 7 3 5 20 | accepting E6F53B54A4814DA2614922482CD00894 1 8 2 8 21 | oppression E6F53B54A4814DA2614922482CD00894 2 1 5 1 22 | subjective E6F53B54A4814DA2614922482CD00894 2 5 6 2 23 | inhumane E6F53B54A4814DA2614922482CD00894 2 1 6 1 24 | inaccurate E6F53B54A4814DA2614922482CD00894 2 2 5 2 25 | wrongfully E6F53B54A4814DA2614922482CD00894 2 2 6 2 26 | bigotry E6F53B54A4814DA2614922482CD00894 2 1 7 1 27 | honest E6F53B54A4814DA2614922482CD00894 1 8 6 9 28 | competent E6F53B54A4814DA2614922482CD00894 1 7 5 9 29 | inhumane CCCE000DABB136037FB67740E90F1EE7 1 3 5 6 30 | subjective CCCE000DABB136037FB67740E90F1EE7 1 5 4 5 31 | impartial CCCE000DABB136037FB67740E90F1EE7 1 6 4 8 32 | consumerism CCCE000DABB136037FB67740E90F1EE7 1 3 5 3 33 | wrongfully CCCE000DABB136037FB67740E90F1EE7 1 3 4 8 34 | competent CCCE000DABB136037FB67740E90F1EE7 1 5 5 4 35 | permanency CCCE000DABB136037FB67740E90F1EE7 1 5 5 4 36 | disproportionately CCCE000DABB136037FB67740E90F1EE7 1 4 5 3 37 | slavery CCCE000DABB136037FB67740E90F1EE7 1 1 7 2 38 | relatively CCCE000DABB136037FB67740E90F1EE7 1 5 5 6 39 | inaccurate CCCE000DABB136037FB67740E90F1EE7 1 4 5 6 40 | confounding CCCE000DABB136037FB67740E90F1EE7 2 5 5 5 41 | disproportionately B778FDC5991619C6D86F71BA87A95FE6 1 2 2 1 42 | impartial B778FDC5991619C6D86F71BA87A95FE6 1 7 2 9 43 | bigotry B778FDC5991619C6D86F71BA87A95FE6 1 1 5 9 44 | impartial 408A5456560E6B6AC49B24B3E1A8C4F5 1 2 2 5 45 | bigotry 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 46 | proportionality 408A5456560E6B6AC49B24B3E1A8C4F5 1 6 5 5 47 | wrongfully 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 48 | amplitude 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 49 | honest 408A5456560E6B6AC49B24B3E1A8C4F5 1 9 9 9 50 | homophobic 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 51 | inaccurate 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 52 | permanency 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 53 | slavery 09E56CFB89A8A152D2997CA77B0483B1 1 2 8 2 54 | honest 09E56CFB89A8A152D2997CA77B0483B1 1 7 4 8 55 | inaccurate 09E56CFB89A8A152D2997CA77B0483B1 1 3 7 3 56 | slavery 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 57 | consumerism 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 58 | competent 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 59 | homophobic 09E56CFB89A8A152D2997CA77B0483B1 1 1 7 4 60 | wrongfully 09E56CFB89A8A152D2997CA77B0483B1 1 3 7 3 61 | subjective 09E56CFB89A8A152D2997CA77B0483B1 1 6 4 3 62 | disproportionately 09E56CFB89A8A152D2997CA77B0483B1 1 3 7 1 63 | consumerism 09E56CFB89A8A152D2997CA77B0483B1 1 5 6 3 64 | accepting 09E56CFB89A8A152D2997CA77B0483B1 1 5 6 7 65 | oppression 09E56CFB89A8A152D2997CA77B0483B1 1 1 8 3 66 | amplitude 09E56CFB89A8A152D2997CA77B0483B1 1 5 5 2 67 | competent 09E56CFB89A8A152D2997CA77B0483B1 1 6 6 7 68 | amplitude 185F6108754E47F75296A701F68364BF 1 6 7 6 69 | homophobic 185F6108754E47F75296A701F68364BF 1 4 6 5 70 | determinant 185F6108754E47F75296A701F68364BF 1 6 6 4 71 | oppression DFD4A3D0BD652239CB642605673A0630 1 2 2 4 72 | inaccurate DFD4A3D0BD652239CB642605673A0630 1 1 1 1 73 | accepting DFD4A3D0BD652239CB642605673A0630 1 9 9 9 74 | confounding DFD4A3D0BD652239CB642605673A0630 1 5 5 5 75 | inhumane DFD4A3D0BD652239CB642605673A0630 1 1 5 1 76 | consumerism DFD4A3D0BD652239CB642605673A0630 1 5 5 9 77 | honest DFD4A3D0BD652239CB642605673A0630 1 9 5 9 78 | bigotry DFD4A3D0BD652239CB642605673A0630 1 1 5 1 79 | amplitude DFD4A3D0BD652239CB642605673A0630 1 5 5 5 80 | homophobic DFD4A3D0BD652239CB642605673A0630 1 3 5 1 81 | proportionality DFD4A3D0BD652239CB642605673A0630 1 5 5 1 82 | slavery DFD4A3D0BD652239CB642605673A0630 1 1 5 1 83 | homophobic 2568370CAB968D0C4B6B1F6757FAC0BD 1 4 5 3 84 | honest 2568370CAB968D0C4B6B1F6757FAC0BD 1 5 5 6 85 | homophobic BD42B12CAFB6BEFEF1673E12A6561B84 2 5 2 5 86 | inhumane A1D4E5551D1C35454170A198274C66BB 1 3 1 9 87 | oppression A1D4E5551D1C35454170A198274C66BB 1 5 1 9 88 | proportionality A1D4E5551D1C35454170A198274C66BB 1 5 1 1 89 | subjective A1D4E5551D1C35454170A198274C66BB 2 5 1 5 90 | homophobic A1D4E5551D1C35454170A198274C66BB 1 5 1 9 91 | honest A1D4E5551D1C35454170A198274C66BB 1 5 1 1 92 | disproportionately A1D4E5551D1C35454170A198274C66BB 1 5 1 3 93 | relatively A1D4E5551D1C35454170A198274C66BB 2 5 1 5 94 | accepting A1D4E5551D1C35454170A198274C66BB 1 5 1 9 95 | confounding A1D4E5551D1C35454170A198274C66BB 2 5 1 5 96 | permanency A1D4E5551D1C35454170A198274C66BB 2 5 1 5 97 | amplitude A1D4E5551D1C35454170A198274C66BB 2 5 1 5 98 | oppression CDE04327B00D294A0EF8E1F83B5905B4 1 5 5 7 99 | determinant CDE04327B00D294A0EF8E1F83B5905B4 1 5 5 8 100 | relatively CDE04327B00D294A0EF8E1F83B5905B4 2 6 5 5 101 | relatively 9ADD0ACCFEDEACE5717A3262B7BF41FB 1 5 4 5 102 | permanency 9ADD0ACCFEDEACE5717A3262B7BF41FB 1 6 3 3 103 | subjective 9ADD0ACCFEDEACE5717A3262B7BF41FB 1 4 2 7 104 | justify 586EF5A47BD329D537DDB3E7D413A871 1 8 6 5 105 | egalitarian 161DEDC32A325513452E1536CCE25861 1 6 6 5 106 | unequal 586EF5A47BD329D537DDB3E7D413A871 1 3 5 6 107 | segregation 586EF5A47BD329D537DDB3E7D413A871 1 5 8 5 108 | fair-minded 161DEDC32A325513452E1536CCE25861 1 4 5 4 109 | equal C481AC42604F49AD60E147E0D48DA9A8 1 5 6 9 110 | constant 315875F669D472421C781AD1E2E6B787 1 6 6 5 111 | tolerant 161DEDC32A325513452E1536CCE25861 1 5 5 5 112 | justification 315875F669D472421C781AD1E2E6B787 1 6 7 5 113 | honesty 161DEDC32A325513452E1536CCE25861 1 5 5 5 114 | segregationism C481AC42604F49AD60E147E0D48DA9A8 1 1 4 1 115 | prejudice 315875F669D472421C781AD1E2E6B787 1 6 6 5 116 | prejudicial 315875F669D472421C781AD1E2E6B787 1 6 6 5 117 | discriminatory C481AC42604F49AD60E147E0D48DA9A8 1 1 4 1 118 | justify 315875F669D472421C781AD1E2E6B787 1 6 6 5 119 | equal 315875F669D472421C781AD1E2E6B787 1 6 6 5 120 | unscrupulous C481AC42604F49AD60E147E0D48DA9A8 1 1 4 3 121 | unequalled 315875F669D472421C781AD1E2E6B787 1 5 5 5 122 | disproportionate 315875F669D472421C781AD1E2E6B787 1 5 4 5 123 | homologous 315875F669D472421C781AD1E2E6B787 1 4 4 4 124 | reasonable 161DEDC32A325513452E1536CCE25861 1 5 5 5 125 | equaliser 8FB661F85CEA9FF87506002B3BFD078E 1 7 7 7 126 | exclusion C481AC42604F49AD60E147E0D48DA9A8 1 1 6 1 127 | fair-minded 8FB661F85CEA9FF87506002B3BFD078E 1 8 8 9 128 | homologous C481AC42604F49AD60E147E0D48DA9A8 1 5 5 1 129 | equalitarian C481AC42604F49AD60E147E0D48DA9A8 1 3 7 5 130 | disproportionate 8FB661F85CEA9FF87506002B3BFD078E 1 4 5 3 131 | discrimination BA45B96CDD19418387D57EEE75A1F127 1 1 8 1 132 | unprejudiced C481AC42604F49AD60E147E0D48DA9A8 1 6 7 7 133 | unprejudiced BA45B96CDD19418387D57EEE75A1F127 1 9 2 9 134 | fair 8FB661F85CEA9FF87506002B3BFD078E 1 8 8 9 135 | preference 161DEDC32A325513452E1536CCE25861 1 5 5 5 136 | tolerant BA45B96CDD19418387D57EEE75A1F127 1 9 2 9 137 | balance 161DEDC32A325513452E1536CCE25861 1 5 5 5 138 | unequalled 15E79E1BDCA40241D5FE6651A2077621 1 5 6 3 139 | prejudicial 15E79E1BDCA40241D5FE6651A2077621 1 1 6 8 140 | unequal BA45B96CDD19418387D57EEE75A1F127 1 1 8 9 141 | prejudice 8FB661F85CEA9FF87506002B3BFD078E 1 4 5 2 142 | preference 15E79E1BDCA40241D5FE6651A2077621 1 2 4 9 143 | disproportion 15E79E1BDCA40241D5FE6651A2077621 1 2 6 1 144 | unprejudiced 8FB661F85CEA9FF87506002B3BFD078E 1 7 8 8 145 | discriminatory 15E79E1BDCA40241D5FE6651A2077621 1 1 6 9 146 | equable 8FB661F85CEA9FF87506002B3BFD078E 1 7 6 6 147 | unscrupulous BA45B96CDD19418387D57EEE75A1F127 1 1 8 1 148 | discrimination 8FB661F85CEA9FF87506002B3BFD078E 1 4 5 2 149 | unprejudiced 161DEDC32A325513452E1536CCE25861 1 0 0 0 150 | unjust 15E79E1BDCA40241D5FE6651A2077621 1 4 8 9 151 | injustice 15E79E1BDCA40241D5FE6651A2077621 1 1 8 9 152 | equalitarian AB3668D1FB431F6CE11568AB689C58C2 1 8 1 1 153 | justification AB3668D1FB431F6CE11568AB689C58C2 1 6 3 9 154 | prejudicial AB3668D1FB431F6CE11568AB689C58C2 1 2 6 8 155 | bigot AB3668D1FB431F6CE11568AB689C58C2 1 8 3 2 156 | equalitarian 4E0C798A73F016100353720FAA644B12 1 7 3 7 157 | equivalent AB3668D1FB431F6CE11568AB689C58C2 1 6 3 1 158 | justice 4E0C798A73F016100353720FAA644B12 1 7 2 8 159 | fair-minded AB3668D1FB431F6CE11568AB689C58C2 1 8 1 9 160 | fair-minded D4633A0AC6ED3DD59D88AC6524AFEE1A 1 7 6 8 161 | reasonable AB3668D1FB431F6CE11568AB689C58C2 1 8 3 1 162 | balance AB3668D1FB431F6CE11568AB689C58C2 1 8 2 1 163 | discrimination 4E0C798A73F016100353720FAA644B12 1 4 5 4 164 | unjust AB3668D1FB431F6CE11568AB689C58C2 1 1 7 9 165 | justificatory D4633A0AC6ED3DD59D88AC6524AFEE1A 1 3 4 3 166 | bigot 4E0C798A73F016100353720FAA644B12 1 2 2 5 167 | unjust D4633A0AC6ED3DD59D88AC6524AFEE1A 1 3 2 7 168 | unscrupulous 4E0C798A73F016100353720FAA644B12 1 3 5 5 169 | equal 4E0C798A73F016100353720FAA644B12 1 7 6 7 170 | egalitarian D4633A0AC6ED3DD59D88AC6524AFEE1A 1 6 5 7 171 | reciprocal 586EF5A47BD329D537DDB3E7D413A871 1 7 7 9 172 | unjustified 4E0C798A73F016100353720FAA644B12 1 5 5 5 173 | injustice 4E0C798A73F016100353720FAA644B12 1 3 5 7 174 | preference D4633A0AC6ED3DD59D88AC6524AFEE1A 1 5 5 5 175 | discriminatory 4E0C798A73F016100353720FAA644B12 1 4 4 4 176 | equity 586EF5A47BD329D537DDB3E7D413A871 1 9 5 9 177 | dishonest D4633A0AC6ED3DD59D88AC6524AFEE1A 1 1 4 3 178 | disproportionate 586EF5A47BD329D537DDB3E7D413A871 1 1 4 1 179 | exclude 586EF5A47BD329D537DDB3E7D413A871 1 4 4 2 180 | justice D4633A0AC6ED3DD59D88AC6524AFEE1A 1 7 6 9 181 | bigot D4633A0AC6ED3DD59D88AC6524AFEE1A 1 5 5 3 182 | equable D4633A0AC6ED3DD59D88AC6524AFEE1A 1 5 5 4 183 | evenness FFF25F05409DE69284DD1F9B8ADD9803 1 5 2 4 184 | unjust FFF25F05409DE69284DD1F9B8ADD9803 1 2 7 8 185 | exclusion FFF25F05409DE69284DD1F9B8ADD9803 1 3 6 4 186 | reciprocal FFF25F05409DE69284DD1F9B8ADD9803 1 6 4 3 187 | prejudicial FFF25F05409DE69284DD1F9B8ADD9803 1 1 7 8 188 | dishonest FFF25F05409DE69284DD1F9B8ADD9803 1 1 7 9 189 | dissociate FFF25F05409DE69284DD1F9B8ADD9803 1 5 5 5 190 | disproportion FFF25F05409DE69284DD1F9B8ADD9803 1 4 5 2 191 | equaliser FFF25F05409DE69284DD1F9B8ADD9803 1 5 5 2 192 | injustice 4535A918C5EBDAA4929D32A143E0B1C2 1 3 5 5 193 | equal 4535A918C5EBDAA4929D32A143E0B1C2 1 7 6 7 194 | balance 4535A918C5EBDAA4929D32A143E0B1C2 1 7 2 8 195 | segregationist 4535A918C5EBDAA4929D32A143E0B1C2 1 5 5 3 196 | unjust 4535A918C5EBDAA4929D32A143E0B1C2 1 3 8 3 197 | discriminatory B778FDC5991619C6D86F71BA87A95FE6 1 4 2 1 198 | justice B778FDC5991619C6D86F71BA87A95FE6 1 7 2 9 199 | constant B778FDC5991619C6D86F71BA87A95FE6 1 6 2 3 200 | reciprocal 4535A918C5EBDAA4929D32A143E0B1C2 1 7 6 8 201 | reciprocal B778FDC5991619C6D86F71BA87A95FE6 1 6 3 7 202 | equaliser 4535A918C5EBDAA4929D32A143E0B1C2 1 7 7 7 203 | honesty B778FDC5991619C6D86F71BA87A95FE6 1 7 2 8 204 | right FB73B91F87BE6E8EA3E745DDA7473E2B 1 7 2 6 205 | equity B778FDC5991619C6D86F71BA87A95FE6 1 8 2 9 206 | unfair FB73B91F87BE6E8EA3E745DDA7473E2B 1 3 6 9 207 | discrimination 4535A918C5EBDAA4929D32A143E0B1C2 1 3 7 2 208 | egalitarian FB73B91F87BE6E8EA3E745DDA7473E2B 1 5 3 2 209 | segregationism B778FDC5991619C6D86F71BA87A95FE6 1 2 4 1 210 | fair-mindedness B778FDC5991619C6D86F71BA87A95FE6 1 8 2 9 211 | unjust FB73B91F87BE6E8EA3E745DDA7473E2B 1 1 7 2 212 | justify FB73B91F87BE6E8EA3E745DDA7473E2B 1 7 4 1 213 | unprejudiced FB73B91F87BE6E8EA3E745DDA7473E2B 1 9 3 9 214 | fair-mindedness FB73B91F87BE6E8EA3E745DDA7473E2B 1 8 5 9 215 | unjustified FB73B91F87BE6E8EA3E745DDA7473E2B 1 5 6 5 216 | fair 0052E79E1291A1A51AB1AB7434A23593 1 9 9 9 217 | exclude 0052E79E1291A1A51AB1AB7434A23593 1 3 6 5 218 | equity 0052E79E1291A1A51AB1AB7434A23593 1 7 7 5 219 | justification 0052E79E1291A1A51AB1AB7434A23593 1 7 7 7 220 | dishonest 0052E79E1291A1A51AB1AB7434A23593 1 1 7 8 221 | bias 0052E79E1291A1A51AB1AB7434A23593 1 3 5 7 222 | dissociate D8F673A7AE6DF6AE253D31E038E1C8F7 1 3 3 1 223 | balance 0052E79E1291A1A51AB1AB7434A23593 1 5 5 5 224 | fair-mindedness 0052E79E1291A1A51AB1AB7434A23593 1 8 8 9 225 | unjustified D8F673A7AE6DF6AE253D31E038E1C8F7 1 2 5 7 226 | discriminatory D8F673A7AE6DF6AE253D31E038E1C8F7 1 2 7 5 227 | prejudgment D531FA7EA3FA78411003699A7D7D4C3B 1 4 5 4 228 | equable D8F673A7AE6DF6AE253D31E038E1C8F7 1 6 5 3 229 | balance D8F673A7AE6DF6AE253D31E038E1C8F7 1 7 2 9 230 | exclude D8F673A7AE6DF6AE253D31E038E1C8F7 1 4 6 4 231 | evenness D8F673A7AE6DF6AE253D31E038E1C8F7 1 8 5 7 232 | equaliser 3356A89117EE8BAFA7D736A5E4965E7D 1 5 3 9 233 | reasonable 3356A89117EE8BAFA7D736A5E4965E7D 1 9 1 2 234 | fair 3356A89117EE8BAFA7D736A5E4965E7D 1 7 2 9 235 | unequalled 3356A89117EE8BAFA7D736A5E4965E7D 1 1 3 5 236 | reciprocal 3356A89117EE8BAFA7D736A5E4965E7D 1 5 5 5 237 | bias 3356A89117EE8BAFA7D736A5E4965E7D 1 5 5 5 238 | constant 3356A89117EE8BAFA7D736A5E4965E7D 1 5 5 5 239 | disproportionate 3356A89117EE8BAFA7D736A5E4965E7D 1 1 1 7 240 | prejudgment 3356A89117EE8BAFA7D736A5E4965E7D 1 5 5 5 241 | exclusion 133A7FC3883AA857DEC2FE736035F72E 1 3 5 2 242 | fair 133A7FC3883AA857DEC2FE736035F72E 1 6 4 9 243 | segregationism 133A7FC3883AA857DEC2FE736035F72E 1 1 5 1 244 | prejudicial 133A7FC3883AA857DEC2FE736035F72E 1 1 5 1 245 | unprejudiced 133A7FC3883AA857DEC2FE736035F72E 1 7 4 8 246 | unfair 133A7FC3883AA857DEC2FE736035F72E 1 3 6 3 247 | equalitarian 133A7FC3883AA857DEC2FE736035F72E 1 8 7 9 248 | dishonest 133A7FC3883AA857DEC2FE736035F72E 1 2 6 3 249 | constant 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 6 5 250 | fair 50F1D17435B53F4BE0F5D1F47C8D7826 1 7 7 9 251 | unequalised 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 5 2 252 | right 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 5 7 253 | justification 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 5 6 254 | prejudgment 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 5 3 255 | segregationism 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 5 3 256 | bigot 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 5 3 257 | unbiased 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 5 8 258 | disproportion 4EAD4A593DD30560FFF6BB3F5E66800B 1 3 3 6 259 | justify 4EAD4A593DD30560FFF6BB3F5E66800B 1 6 6 8 260 | equivalent 4EAD4A593DD30560FFF6BB3F5E66800B 1 5 4 4 261 | unequalled 4EAD4A593DD30560FFF6BB3F5E66800B 1 4 4 3 262 | exclusion 9C2E01F223AE7768BC0D9843A50A88A5 1 3 3 8 263 | prejudice 4EAD4A593DD30560FFF6BB3F5E66800B 1 2 2 7 264 | discrimination 9C2E01F223AE7768BC0D9843A50A88A5 1 3 4 3 265 | exclude 9C2E01F223AE7768BC0D9843A50A88A5 1 4 4 3 266 | egalitarian 9C2E01F223AE7768BC0D9843A50A88A5 1 7 6 9 267 | fair-minded 4EAD4A593DD30560FFF6BB3F5E66800B 1 6 4 8 268 | dishonest 9C2E01F223AE7768BC0D9843A50A88A5 1 3 4 3 269 | discrimination 4EAD4A593DD30560FFF6BB3F5E66800B 1 4 3 8 270 | bias 4EAD4A593DD30560FFF6BB3F5E66800B 1 3 5 8 271 | exclude 4EAD4A593DD30560FFF6BB3F5E66800B 1 3 5 7 272 | dissociate CB72D1B4E93AF561AC69D87B79B22F1C 1 5 5 5 273 | segregationism CB72D1B4E93AF561AC69D87B79B22F1C 1 5 6 5 274 | equaliser CB72D1B4E93AF561AC69D87B79B22F1C 1 5 6 6 275 | unequalled CB72D1B4E93AF561AC69D87B79B22F1C 1 5 5 5 276 | prejudgment CB72D1B4E93AF561AC69D87B79B22F1C 1 5 5 3 277 | egalitarian CB72D1B4E93AF561AC69D87B79B22F1C 1 5 5 7 278 | unequal C01309EFB777076FE6AD43225A49D457 1 5 5 3 279 | disproportion C01309EFB777076FE6AD43225A49D457 1 5 5 2 280 | disproportion CB72D1B4E93AF561AC69D87B79B22F1C 1 5 5 4 281 | exclusion CB72D1B4E93AF561AC69D87B79B22F1C 1 5 5 3 282 | unfair CB72D1B4E93AF561AC69D87B79B22F1C 1 5 5 2 283 | equable 9C2E01F223AE7768BC0D9843A50A88A5 1 4 6 9 284 | unjustified 9C2E01F223AE7768BC0D9843A50A88A5 1 3 7 6 285 | segregationist C01309EFB777076FE6AD43225A49D457 1 5 5 4 286 | unequal 9C2E01F223AE7768BC0D9843A50A88A5 1 4 5 7 287 | justify C01309EFB777076FE6AD43225A49D457 1 6 6 8 288 | bigot 9C2E01F223AE7768BC0D9843A50A88A5 1 3 7 5 289 | justification C01309EFB777076FE6AD43225A49D457 1 5 5 8 290 | unfair C01309EFB777076FE6AD43225A49D457 1 4 5 8 291 | reasonable C01309EFB777076FE6AD43225A49D457 1 5 5 5 292 | disproportionate C01309EFB777076FE6AD43225A49D457 1 5 5 2 293 | dissociate FDEDCF7929B94077E133985905041C7C 1 5 1 2 294 | justice FDEDCF7929B94077E133985905041C7C 1 5 3 3 295 | bias FDEDCF7929B94077E133985905041C7C 1 1 4 5 296 | fair-mindedness FDEDCF7929B94077E133985905041C7C 1 8 4 9 297 | unscrupulous FDEDCF7929B94077E133985905041C7C 1 1 6 1 298 | unjustified FDEDCF7929B94077E133985905041C7C 1 1 3 1 299 | segregationist FDEDCF7929B94077E133985905041C7C 1 1 7 1 300 | equivalent FDEDCF7929B94077E133985905041C7C 1 5 2 8 301 | equable FDEDCF7929B94077E133985905041C7C 1 7 2 9 302 | prejudgment ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 3 8 4 303 | unfair ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 1 9 1 304 | unbiased ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 9 9 9 305 | tolerant ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 4 6 8 306 | right ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 9 7 9 307 | evenness ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 8 8 7 308 | segregation ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 4 7 9 309 | honesty ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 9 9 9 310 | unbiased 208CA3F1150088304C597D9E4B2AB24A 1 8 8 8 311 | segregationist 208CA3F1150088304C597D9E4B2AB24A 1 2 4 2 312 | honesty 208CA3F1150088304C597D9E4B2AB24A 1 9 9 9 313 | equalitarian 208CA3F1150088304C597D9E4B2AB24A 1 9 8 9 314 | reasonable 208CA3F1150088304C597D9E4B2AB24A 1 8 9 8 315 | constant 208CA3F1150088304C597D9E4B2AB24A 1 5 5 5 316 | evenness D5149EDB625CB2E8B60423F7A3F16AC9 1 8 8 1 317 | equity D5149EDB625CB2E8B60423F7A3F16AC9 1 6 8 1 318 | prejudice 208CA3F1150088304C597D9E4B2AB24A 1 1 4 1 319 | prejudice D5149EDB625CB2E8B60423F7A3F16AC9 1 5 7 7 320 | unequal D5149EDB625CB2E8B60423F7A3F16AC9 1 9 9 1 321 | honesty D5149EDB625CB2E8B60423F7A3F16AC9 1 7 8 9 322 | right 208CA3F1150088304C597D9E4B2AB24A 1 9 9 9 323 | fair-mindedness D5149EDB625CB2E8B60423F7A3F16AC9 1 9 9 9 324 | unbiased D5149EDB625CB2E8B60423F7A3F16AC9 1 9 8 9 325 | equivalent D5149EDB625CB2E8B60423F7A3F16AC9 1 9 9 9 326 | reasonable D5149EDB625CB2E8B60423F7A3F16AC9 1 9 7 9 327 | justificatory 208CA3F1150088304C597D9E4B2AB24A 1 7 6 8 328 | unscrupulous 3134B955E6ED26C197D910E3C3EA2C9A 1 3 6 6 329 | segregation 3134B955E6ED26C197D910E3C3EA2C9A 1 3 5 2 330 | tolerant 3134B955E6ED26C197D910E3C3EA2C9A 1 8 2 9 331 | segregationist 3134B955E6ED26C197D910E3C3EA2C9A 1 4 6 4 332 | prejudgment 3134B955E6ED26C197D910E3C3EA2C9A 1 4 7 8 333 | equity 3134B955E6ED26C197D910E3C3EA2C9A 1 5 5 5 334 | evenness 3134B955E6ED26C197D910E3C3EA2C9A 1 8 2 9 335 | dissociate 3134B955E6ED26C197D910E3C3EA2C9A 1 5 5 5 336 | injustice 3134B955E6ED26C197D910E3C3EA2C9A 1 1 8 9 -------------------------------------------------------------------------------- /moralstrength_raw/RAW_ANNOTATIONS/filtered_annotators/RAW_ANNOTATIONS_CARE.txt: -------------------------------------------------------------------------------- 1 | WORD ANNOTATOR_ID RELEVANCE VALENCE AROUSAL EXPRESSED_MORAL 2 | disgusted DF566D854E2A01B124DE0782BD72F310 1 4 5 3 3 | refuge 7FD550FC9F3A3FEAA99219589E879A56 1 2 2 7 4 | healthcare 7FD550FC9F3A3FEAA99219589E879A56 1 9 9 9 5 | quarrel DF566D854E2A01B124DE0782BD72F310 1 3 5 3 6 | leverage 7FD550FC9F3A3FEAA99219589E879A56 1 7 8 8 7 | refuge 96CB1B215FF34C844A7538DDFB833BA1 1 5 5 8 8 | annihilation DF566D854E2A01B124DE0782BD72F310 1 3 5 1 9 | upsetting 5F42BCE5CA70C6DDA097F36509A48299 1 5 5 5 10 | quarrel 7FD550FC9F3A3FEAA99219589E879A56 1 1 3 2 11 | preserving 7FD550FC9F3A3FEAA99219589E879A56 2 6 7 7 12 | opponent 5F42BCE5CA70C6DDA097F36509A48299 1 5 5 3 13 | collateral DF566D854E2A01B124DE0782BD72F310 1 4 5 4 14 | exempt 7FD550FC9F3A3FEAA99219589E879A56 2 7 8 8 15 | intrusive DF566D854E2A01B124DE0782BD72F310 1 3 5 3 16 | opponent DF566D854E2A01B124DE0782BD72F310 1 5 5 4 17 | destruction 5F42BCE5CA70C6DDA097F36509A48299 1 5 5 2 18 | destruction 7FD550FC9F3A3FEAA99219589E879A56 1 1 2 1 19 | leverage DF566D854E2A01B124DE0782BD72F310 2 5 5 5 20 | opponent B235DDECAE2B238D44A78658FB2FD532 2 6 6 5 21 | abuse 7FD550FC9F3A3FEAA99219589E879A56 1 2 1 3 22 | wrecked 7FD550FC9F3A3FEAA99219589E879A56 1 2 2 1 23 | disgusted D99C5B53A9E5C66EBB54778750BB0CFB 2 2 3 5 24 | wrecked DF566D854E2A01B124DE0782BD72F310 1 4 6 3 25 | opponent 7FD550FC9F3A3FEAA99219589E879A56 1 4 4 3 26 | destruction B235DDECAE2B238D44A78658FB2FD532 1 1 5 1 27 | disgusted 7FD550FC9F3A3FEAA99219589E879A56 1 4 5 3 28 | refuge D99C5B53A9E5C66EBB54778750BB0CFB 2 6 3 7 29 | preserving DF566D854E2A01B124DE0782BD72F310 1 5 4 6 30 | destruction 96CB1B215FF34C844A7538DDFB833BA1 1 1 4 1 31 | critically 7FD550FC9F3A3FEAA99219589E879A56 2 5 6 6 32 | upsetting 7FD550FC9F3A3FEAA99219589E879A56 1 3 3 3 33 | broken DF566D854E2A01B124DE0782BD72F310 1 4 4 2 34 | annihilation B235DDECAE2B238D44A78658FB2FD532 1 2 5 1 35 | bang 7FD550FC9F3A3FEAA99219589E879A56 1 2 2 1 36 | critically 96CB1B215FF34C844A7538DDFB833BA1 1 2 3 1 37 | destruction DF566D854E2A01B124DE0782BD72F310 1 2 6 1 38 | dignity 7FD550FC9F3A3FEAA99219589E879A56 2 7 8 8 39 | critically 5F42BCE5CA70C6DDA097F36509A48299 1 4 5 3 40 | upsetting D99C5B53A9E5C66EBB54778750BB0CFB 2 5 4 4 41 | intrusive 7FD550FC9F3A3FEAA99219589E879A56 1 2 2 1 42 | refuge DF566D854E2A01B124DE0782BD72F310 1 4 5 4 43 | dignity 96CB1B215FF34C844A7538DDFB833BA1 1 7 5 9 44 | assassin 7FD550FC9F3A3FEAA99219589E879A56 1 1 1 1 45 | leverage 5F42BCE5CA70C6DDA097F36509A48299 2 5 5 5 46 | upsetting 96CB1B215FF34C844A7538DDFB833BA1 1 2 4 2 47 | collateral 7FD550FC9F3A3FEAA99219589E879A56 1 2 3 2 48 | abuse 5F42BCE5CA70C6DDA097F36509A48299 1 3 5 2 49 | critically DF566D854E2A01B124DE0782BD72F310 1 4 6 4 50 | annihilation 7FD550FC9F3A3FEAA99219589E879A56 1 1 1 1 51 | broken 7FD550FC9F3A3FEAA99219589E879A56 1 2 2 2 52 | assassin DF566D854E2A01B124DE0782BD72F310 1 1 7 1 53 | quarrel 96CB1B215FF34C844A7538DDFB833BA1 1 1 3 1 54 | annihilation 5F42BCE5CA70C6DDA097F36509A48299 1 3 5 2 55 | opponent 96CB1B215FF34C844A7538DDFB833BA1 1 1 4 1 56 | abuse DF566D854E2A01B124DE0782BD72F310 1 1 6 1 57 | disgusted 96CB1B215FF34C844A7538DDFB833BA1 1 1 5 2 58 | quarrel 5F42BCE5CA70C6DDA097F36509A48299 1 4 5 2 59 | opponent D99C5B53A9E5C66EBB54778750BB0CFB 1 4 4 5 60 | collateral 96CB1B215FF34C844A7538DDFB833BA1 1 7 5 9 61 | exempt 96CB1B215FF34C844A7538DDFB833BA1 1 5 5 5 62 | healthcare DF566D854E2A01B124DE0782BD72F310 1 5 4 8 63 | healthcare 5F42BCE5CA70C6DDA097F36509A48299 1 7 5 9 64 | bang DF566D854E2A01B124DE0782BD72F310 2 5 5 5 65 | preserving 5F42BCE5CA70C6DDA097F36509A48299 1 6 5 8 66 | healthcare 96CB1B215FF34C844A7538DDFB833BA1 1 9 7 9 67 | wrecked D99C5B53A9E5C66EBB54778750BB0CFB 2 5 5 4 68 | abuse 96CB1B215FF34C844A7538DDFB833BA1 1 2 3 1 69 | dignity DF566D854E2A01B124DE0782BD72F310 1 4 4 6 70 | assassin 96CB1B215FF34C844A7538DDFB833BA1 1 1 1 1 71 | exempt B235DDECAE2B238D44A78658FB2FD532 2 5 5 5 72 | exempt DF566D854E2A01B124DE0782BD72F310 2 4 6 5 73 | annihilation D99C5B53A9E5C66EBB54778750BB0CFB 1 1 6 1 74 | abuse B235DDECAE2B238D44A78658FB2FD532 1 2 5 2 75 | abuse D99C5B53A9E5C66EBB54778750BB0CFB 1 1 6 1 76 | assassin 5F42BCE5CA70C6DDA097F36509A48299 1 3 5 1 77 | broken 96CB1B215FF34C844A7538DDFB833BA1 1 2 2 1 78 | dignity D99C5B53A9E5C66EBB54778750BB0CFB 1 7 6 7 79 | preserving 96CB1B215FF34C844A7538DDFB833BA1 1 7 5 8 80 | upsetting 262409BDF797CBCE4879235E26239572 1 4 6 4 81 | dignity 5F42BCE5CA70C6DDA097F36509A48299 1 5 5 6 82 | upsetting DF566D854E2A01B124DE0782BD72F310 1 3 5 4 83 | collateral B235DDECAE2B238D44A78658FB2FD532 1 3 5 2 84 | wrecked 96CB1B215FF34C844A7538DDFB833BA1 1 1 2 1 85 | refuge 5F42BCE5CA70C6DDA097F36509A48299 1 6 5 8 86 | upsetting B235DDECAE2B238D44A78658FB2FD532 1 3 5 5 87 | intrusive D99C5B53A9E5C66EBB54778750BB0CFB 1 3 6 3 88 | broken 5F42BCE5CA70C6DDA097F36509A48299 1 4 5 3 89 | destruction 262409BDF797CBCE4879235E26239572 1 1 7 1 90 | broken B235DDECAE2B238D44A78658FB2FD532 1 3 5 3 91 | collateral D99C5B53A9E5C66EBB54778750BB0CFB 1 5 5 5 92 | preserving B235DDECAE2B238D44A78658FB2FD532 1 6 5 7 93 | wrecked 5F42BCE5CA70C6DDA097F36509A48299 2 5 5 5 94 | assassin 262409BDF797CBCE4879235E26239572 1 2 7 1 95 | wrecked B235DDECAE2B238D44A78658FB2FD532 1 2 5 2 96 | disgusted 5F42BCE5CA70C6DDA097F36509A48299 1 4 5 3 97 | assassin B235DDECAE2B238D44A78658FB2FD532 1 1 5 1 98 | healthcare D99C5B53A9E5C66EBB54778750BB0CFB 2 5 5 5 99 | critically D99C5B53A9E5C66EBB54778750BB0CFB 1 4 6 4 100 | collateral 5F42BCE5CA70C6DDA097F36509A48299 2 5 5 5 101 | broken D99C5B53A9E5C66EBB54778750BB0CFB 2 5 5 5 102 | healthcare B235DDECAE2B238D44A78658FB2FD532 1 6 6 8 103 | assassin D99C5B53A9E5C66EBB54778750BB0CFB 1 1 6 1 104 | leverage B235DDECAE2B238D44A78658FB2FD532 2 5 5 5 105 | intrusive 262409BDF797CBCE4879235E26239572 1 3 6 3 106 | dignity B235DDECAE2B238D44A78658FB2FD532 2 5 5 5 107 | abuse 262409BDF797CBCE4879235E26239572 1 2 7 2 108 | destruction D99C5B53A9E5C66EBB54778750BB0CFB 1 2 6 1 109 | exempt D99C5B53A9E5C66EBB54778750BB0CFB 2 5 5 5 110 | intrusive B235DDECAE2B238D44A78658FB2FD532 1 3 5 3 111 | broken 262409BDF797CBCE4879235E26239572 1 2 7 2 112 | quarrel D99C5B53A9E5C66EBB54778750BB0CFB 1 1 6 1 113 | collateral 262409BDF797CBCE4879235E26239572 1 1 7 1 114 | preserving D99C5B53A9E5C66EBB54778750BB0CFB 1 7 4 8 115 | bang B235DDECAE2B238D44A78658FB2FD532 2 5 5 5 116 | disgusted 262409BDF797CBCE4879235E26239572 1 4 7 3 117 | disgusted B235DDECAE2B238D44A78658FB2FD532 2 5 5 5 118 | critically 262409BDF797CBCE4879235E26239572 1 4 5 4 119 | opponent 262409BDF797CBCE4879235E26239572 2 5 5 5 120 | quarrel B235DDECAE2B238D44A78658FB2FD532 1 1 5 2 121 | annihilation 262409BDF797CBCE4879235E26239572 1 2 7 1 122 | refuge B235DDECAE2B238D44A78658FB2FD532 2 5 5 5 123 | leverage 262409BDF797CBCE4879235E26239572 2 5 5 5 124 | exempt 262409BDF797CBCE4879235E26239572 2 5 5 5 125 | bang 262409BDF797CBCE4879235E26239572 1 2 7 1 126 | bang 0A828669E7EE548539ACC3852B37A368 1 3 7 1 127 | safeguard 864C97282747D4BEB83D96B29067CE10 1 9 3 9 128 | ravage 864C97282747D4BEB83D96B29067CE10 1 4 4 1 129 | safehold 864C97282747D4BEB83D96B29067CE10 1 7 2 9 130 | endangered 864C97282747D4BEB83D96B29067CE10 1 4 4 1 131 | violence 864C97282747D4BEB83D96B29067CE10 1 4 4 1 132 | guard 15E79E1BDCA40241D5FE6651A2077621 1 7 7 8 133 | cruelly 15E79E1BDCA40241D5FE6651A2077621 1 1 9 1 134 | protection 15E79E1BDCA40241D5FE6651A2077621 1 8 3 8 135 | hurt 1D420B59E8FA52FA98B2C1025327A784 1 4 7 6 136 | abuse 15E79E1BDCA40241D5FE6651A2077621 1 1 9 1 137 | compassion 1D420B59E8FA52FA98B2C1025327A784 1 6 4 5 138 | ravage 15E79E1BDCA40241D5FE6651A2077621 1 2 8 4 139 | brutal 1D420B59E8FA52FA98B2C1025327A784 1 6 7 3 140 | safeguard 1D420B59E8FA52FA98B2C1025327A784 1 3 6 6 141 | violent 1D420B59E8FA52FA98B2C1025327A784 1 6 6 4 142 | defenestration 1D420B59E8FA52FA98B2C1025327A784 1 1 1 1 143 | wound E935E44C874CAA83E12C7E40BDF97DBD 1 3 5 1 144 | protectionist E935E44C874CAA83E12C7E40BDF97DBD 1 8 2 9 145 | protector E935E44C874CAA83E12C7E40BDF97DBD 1 9 2 9 146 | ruin 4E0C798A73F016100353720FAA644B12 1 2 5 2 147 | stomp E935E44C874CAA83E12C7E40BDF97DBD 1 5 5 5 148 | damage E935E44C874CAA83E12C7E40BDF97DBD 1 2 5 1 149 | abandon 4E0C798A73F016100353720FAA644B12 1 3 4 2 150 | amity 4E0C798A73F016100353720FAA644B12 1 7 2 7 151 | security 4E0C798A73F016100353720FAA644B12 1 7 2 8 152 | defenestration 4E0C798A73F016100353720FAA644B12 1 5 5 3 153 | cruelly B439BD3CB1535E50C4C0662243C4134B 1 2 6 2 154 | violence B439BD3CB1535E50C4C0662243C4134B 1 2 8 1 155 | crusher B439BD3CB1535E50C4C0662243C4134B 1 1 6 3 156 | amity 1A3C93478F821F54774A88612C46BF7B 1 5 5 5 157 | peacetime B439BD3CB1535E50C4C0662243C4134B 1 5 5 5 158 | abuse B439BD3CB1535E50C4C0662243C4134B 1 2 8 1 159 | sympathy 1A3C93478F821F54774A88612C46BF7B 1 7 3 9 160 | brutal 1A3C93478F821F54774A88612C46BF7B 1 2 5 1 161 | fight 22F30BF330426F4477A0CCF7A6AFF93C 1 7 7 7 162 | safeguard 22F30BF330426F4477A0CCF7A6AFF93C 1 8 6 6 163 | benefit 0C7D1AE71B8634CEEE95416CCD28E812 1 6 6 7 164 | violent 22F30BF330426F4477A0CCF7A6AFF93C 1 4 4 3 165 | ruin D6F12FBD96E07B40FE03E40D4F91D0C8 1 2 5 1 166 | peacenik D6F12FBD96E07B40FE03E40D4F91D0C8 1 5 5 8 167 | peace 0C7D1AE71B8634CEEE95416CCD28E812 1 7 7 5 168 | preserve D6F12FBD96E07B40FE03E40D4F91D0C8 1 6 5 8 169 | benefit 22F30BF330426F4477A0CCF7A6AFF93C 1 7 8 7 170 | attack 22F30BF330426F4477A0CCF7A6AFF93C 1 6 7 8 171 | compassion 0C7D1AE71B8634CEEE95416CCD28E812 1 7 4 6 172 | empathy D6F12FBD96E07B40FE03E40D4F91D0C8 1 6 5 8 173 | safeguard 0C7D1AE71B8634CEEE95416CCD28E812 1 7 7 6 174 | impair D6F12FBD96E07B40FE03E40D4F91D0C8 1 3 5 3 175 | violent 0C7D1AE71B8634CEEE95416CCD28E812 1 3 2 7 176 | shelter 0C7D1AE71B8634CEEE95416CCD28E812 1 6 5 6 177 | sympathetic 0C7D1AE71B8634CEEE95416CCD28E812 1 7 7 7 178 | damage 0C7D1AE71B8634CEEE95416CCD28E812 1 4 3 3 179 | destroy 56A99CDD4FBB62AE258F430EE5128D73 1 1 6 1 180 | violent 56A99CDD4FBB62AE258F430EE5128D73 1 3 6 1 181 | annihilation 56A99CDD4FBB62AE258F430EE5128D73 1 2 6 1 182 | annihilation 31BF666100C3A5BABAB95D40D7BFDE9D 1 1 9 1 183 | empathetic 56A99CDD4FBB62AE258F430EE5128D73 1 7 5 7 184 | fight 56A99CDD4FBB62AE258F430EE5128D73 1 4 7 2 185 | sympathetic FE5A1B7ABCEC1DD1028CE61AA6E08431 1 9 5 9 186 | violent 31BF666100C3A5BABAB95D40D7BFDE9D 1 2 6 1 187 | war 31BF666100C3A5BABAB95D40D7BFDE9D 1 1 9 1 188 | damage 31BF666100C3A5BABAB95D40D7BFDE9D 1 1 8 1 189 | compassion 31BF666100C3A5BABAB95D40D7BFDE9D 1 8 2 9 190 | spurn 586EF5A47BD329D537DDB3E7D413A871 1 7 5 2 191 | attack 586EF5A47BD329D537DDB3E7D413A871 1 3 5 4 192 | wound 586EF5A47BD329D537DDB3E7D413A871 1 2 5 3 193 | benefit 17B3FBC4AC55D595225F84535BE4F193 1 8 4 9 194 | guard 8FB6081DC6ED196F4F98E7C49ABC5C19 1 5 4 7 195 | killer 17B3FBC4AC55D595225F84535BE4F193 1 1 5 1 196 | peaceable 586EF5A47BD329D537DDB3E7D413A871 1 6 4 7 197 | protectionist 586EF5A47BD329D537DDB3E7D413A871 1 6 5 8 198 | attack 17B3FBC4AC55D595225F84535BE4F193 1 2 8 1 199 | impair 17B3FBC4AC55D595225F84535BE4F193 1 1 2 2 200 | defendant 8FB6081DC6ED196F4F98E7C49ABC5C19 1 4 5 3 201 | sympathetic 17B3FBC4AC55D595225F84535BE4F193 1 7 2 9 202 | care 8FB6081DC6ED196F4F98E7C49ABC5C19 1 5 6 8 203 | stomp 3878807D71C2653AB071D55EAE734844 1 5 6 3 204 | ruin 3878807D71C2653AB071D55EAE734844 1 3 5 1 205 | crush 8FB6081DC6ED196F4F98E7C49ABC5C19 1 4 6 2 206 | impair 3878807D71C2653AB071D55EAE734844 1 3 5 1 207 | hurt 8FB6081DC6ED196F4F98E7C49ABC5C19 1 2 7 3 208 | safety 3878807D71C2653AB071D55EAE734844 1 6 4 9 209 | ravage 3878807D71C2653AB071D55EAE734844 1 4 7 2 210 | abandon A7B9A613CF8F39A8815F6C2F66F0A6C4 1 1 6 1 211 | safekeeping A7B9A613CF8F39A8815F6C2F66F0A6C4 1 6 3 9 212 | protection 3B421D473099DBDE85779319E217C1DD 1 8 7 7 213 | shield 3B421D473099DBDE85779319E217C1DD 1 7 7 9 214 | empathy A7B9A613CF8F39A8815F6C2F66F0A6C4 1 7 5 9 215 | crusher 3B421D473099DBDE85779319E217C1DD 1 3 3 2 216 | empathetic 3B421D473099DBDE85779319E217C1DD 1 5 5 6 217 | war 3B421D473099DBDE85779319E217C1DD 1 1 5 1 218 | benefit 3CDD533C51DD875FFF5047BC36C8013C 1 9 9 7 219 | safe 3CDD533C51DD875FFF5047BC36C8013C 1 9 1 9 220 | protector A7B9A613CF8F39A8815F6C2F66F0A6C4 1 9 1 9 221 | preserve A7B9A613CF8F39A8815F6C2F66F0A6C4 1 5 3 9 222 | damage 3CDD533C51DD875FFF5047BC36C8013C 1 1 5 9 223 | cruelty 3CDD533C51DD875FFF5047BC36C8013C 1 1 5 4 224 | crush 3CDD533C51DD875FFF5047BC36C8013C 1 1 5 5 225 | protectorship 382235231A1554D2CF102326A186B317 1 7 7 8 226 | care 382235231A1554D2CF102326A186B317 1 9 6 9 227 | safekeeping 382235231A1554D2CF102326A186B317 1 8 2 9 228 | hurt 382235231A1554D2CF102326A186B317 1 3 7 1 229 | crush 382235231A1554D2CF102326A186B317 1 2 7 2 230 | safety 2D587D9C1DA11AADB7C472CBF50C3C55 1 9 9 9 231 | war 2D587D9C1DA11AADB7C472CBF50C3C55 1 2 2 1 232 | abuse 2D587D9C1DA11AADB7C472CBF50C3C55 1 1 1 1 233 | abandon 2D587D9C1DA11AADB7C472CBF50C3C55 1 1 1 1 234 | kill 2D587D9C1DA11AADB7C472CBF50C3C55 1 1 9 1 235 | compassionate 2D587D9C1DA11AADB7C472CBF50C3C55 1 9 9 9 236 | destroy CEF0FB90AC6E8D13EC867A7D9A134C76 1 2 6 1 237 | hurt CEF0FB90AC6E8D13EC867A7D9A134C76 1 3 6 1 238 | peacetime 640B80C3C13FEA2C95C24E3617E97668 1 7 2 9 239 | war 640B80C3C13FEA2C95C24E3617E97668 1 1 5 1 240 | defendant 640B80C3C13FEA2C95C24E3617E97668 1 4 6 3 241 | impair 640B80C3C13FEA2C95C24E3617E97668 1 5 4 5 242 | attack 640B80C3C13FEA2C95C24E3617E97668 1 1 7 1 243 | protectorship 4AC6E9698ACA589293CD56A916EBB9E3 1 6 3 9 244 | damage 4AC6E9698ACA589293CD56A916EBB9E3 1 1 5 1 245 | destroy 4AC6E9698ACA589293CD56A916EBB9E3 1 1 5 1 246 | kill D48AC4E9D32D107E33F6CA9E6B109024 1 1 5 1 247 | sympathetic D48AC4E9D32D107E33F6CA9E6B109024 1 9 1 9 248 | shelter B67FC86A263C6CB5803272EE63C3B8E3 1 6 5 9 249 | detrimental 4AC6E9698ACA589293CD56A916EBB9E3 1 3 5 2 250 | defense 4AC6E9698ACA589293CD56A916EBB9E3 1 5 5 5 251 | killer D48AC4E9D32D107E33F6CA9E6B109024 1 1 5 1 252 | impair B67FC86A263C6CB5803272EE63C3B8E3 1 2 7 2 253 | attack D48AC4E9D32D107E33F6CA9E6B109024 1 1 5 1 254 | preserve AFB3DCFABE98794788FEFCA2B82343DF 1 5 6 5 255 | wound D48AC4E9D32D107E33F6CA9E6B109024 1 1 6 1 256 | benefit B67FC86A263C6CB5803272EE63C3B8E3 1 6 6 7 257 | violence AFB3DCFABE98794788FEFCA2B82343DF 1 4 5 3 258 | guard B67FC86A263C6CB5803272EE63C3B8E3 1 6 6 7 259 | detrimental AFB3DCFABE98794788FEFCA2B82343DF 1 3 4 3 260 | empathetic 20D8918E6756A92FA7BA0F1FD3D65E38 1 8 1 7 261 | endangered 927C72C515189A6068C4C3A5180CEAED 1 3 5 7 262 | peacenik AFB3DCFABE98794788FEFCA2B82343DF 1 6 4 7 263 | peacemaker 20D8918E6756A92FA7BA0F1FD3D65E38 1 7 1 9 264 | crush 395073F7283E212507004C12C8016C8A 1 2 5 2 265 | brutal AFB3DCFABE98794788FEFCA2B82343DF 1 3 3 2 266 | preserve 395073F7283E212507004C12C8016C8A 1 6 7 9 267 | wound 395073F7283E212507004C12C8016C8A 1 2 7 2 268 | compassion 395073F7283E212507004C12C8016C8A 1 7 4 8 269 | shelter 927C72C515189A6068C4C3A5180CEAED 1 8 8 7 270 | detrimental 20D8918E6756A92FA7BA0F1FD3D65E38 1 3 5 2 271 | protection 20D8918E6756A92FA7BA0F1FD3D65E38 1 8 2 9 272 | care 927C72C515189A6068C4C3A5180CEAED 1 9 1 9 273 | safety 20D8918E6756A92FA7BA0F1FD3D65E38 1 8 1 9 274 | safehold 36FD03A62126D82AB9487AAB3DE9C175 1 7 6 8 275 | preserve 36FD03A62126D82AB9487AAB3DE9C175 1 6 6 7 276 | crush 36FD03A62126D82AB9487AAB3DE9C175 1 2 3 1 277 | brutal 927C72C515189A6068C4C3A5180CEAED 1 1 5 1 278 | protectionist 36FD03A62126D82AB9487AAB3DE9C175 1 7 7 7 279 | sympathize 36FD03A62126D82AB9487AAB3DE9C175 1 7 6 7 280 | safety BCC3FDECA7E357C86CE9B1AA10187BDF 1 5 6 5 281 | peace BCC3FDECA7E357C86CE9B1AA10187BDF 1 6 7 8 282 | violence BCC3FDECA7E357C86CE9B1AA10187BDF 1 5 6 3 283 | care 4AC6E9698ACA589293CD56A916EBB9E3 1 9 5 9 284 | endangerment 4AC6E9698ACA589293CD56A916EBB9E3 1 1 1 1 285 | abandon 4AC6E9698ACA589293CD56A916EBB9E3 1 1 5 1 286 | crusher BCC3FDECA7E357C86CE9B1AA10187BDF 1 5 6 5 287 | protectionist 4AC6E9698ACA589293CD56A916EBB9E3 1 9 5 9 288 | sympathize 4AC6E9698ACA589293CD56A916EBB9E3 1 9 1 9 289 | hurt 4AC6E9698ACA589293CD56A916EBB9E3 1 2 5 2 290 | brutalise 4AC6E9698ACA589293CD56A916EBB9E3 1 2 5 2 291 | cruelly 4AC6E9698ACA589293CD56A916EBB9E3 1 2 5 2 292 | abuse 4AC6E9698ACA589293CD56A916EBB9E3 1 1 5 1 293 | abandon 22DE6B567ADECD8F0AC434D54A01AF24 1 3 3 3 294 | protectionism 5DD02FF3C1104891DFD8AC3491233BDC 1 7 4 5 295 | safeguard 22DE6B567ADECD8F0AC434D54A01AF24 1 7 7 3 296 | endangerment 5DD02FF3C1104891DFD8AC3491233BDC 1 4 6 5 297 | shelter 5DD02FF3C1104891DFD8AC3491233BDC 1 5 6 4 298 | brutalise D84064788C4ABD566A7A8653FBA4158A 1 1 3 1 299 | protection 22DE6B567ADECD8F0AC434D54A01AF24 1 7 8 8 300 | safety D84064788C4ABD566A7A8653FBA4158A 1 9 1 9 301 | safehold 5DD02FF3C1104891DFD8AC3491233BDC 1 5 8 6 302 | abuse 5DD02FF3C1104891DFD8AC3491233BDC 1 5 7 6 303 | spurn 90FA06BA5A8D5877F48230D349D517E0 1 2 2 1 304 | crusher 22DE6B567ADECD8F0AC434D54A01AF24 1 7 6 3 305 | safekeeping D84064788C4ABD566A7A8653FBA4158A 1 9 9 9 306 | peacekeeping 90FA06BA5A8D5877F48230D349D517E0 1 8 8 9 307 | defenseless 22DE6B567ADECD8F0AC434D54A01AF24 1 2 3 2 308 | protectorship D84064788C4ABD566A7A8653FBA4158A 1 9 9 9 309 | peace D84064788C4ABD566A7A8653FBA4158A 1 9 9 9 310 | defendant 90FA06BA5A8D5877F48230D349D517E0 1 3 4 3 311 | wound 88D7DF2C238388FF7CCC72E40A9B4577 1 3 4 4 312 | peacekeeping 88D7DF2C238388FF7CCC72E40A9B4577 1 6 3 8 313 | peacemaker 88D7DF2C238388FF7CCC72E40A9B4577 1 7 3 8 314 | protector 2093BA6A9F5529E48FDC8890ADA8AC36 1 8 2 9 315 | exploit 2093BA6A9F5529E48FDC8890ADA8AC36 1 1 6 2 316 | kill 88D7DF2C238388FF7CCC72E40A9B4577 1 4 7 1 317 | safekeeping 88D7DF2C238388FF7CCC72E40A9B4577 1 7 4 8 318 | peaceable 2093BA6A9F5529E48FDC8890ADA8AC36 1 9 1 9 319 | peacekeeping 328DA17593A61A80331F656D235924D7 1 7 7 9 320 | brutalise 328DA17593A61A80331F656D235924D7 1 1 6 1 321 | peaceable 328DA17593A61A80331F656D235924D7 1 6 4 5 322 | amity BF74AAE3CFDF7F526F608BD094BC9CAA 1 5 7 6 323 | suffer BF74AAE3CFDF7F526F608BD094BC9CAA 1 5 7 6 324 | defendant BF74AAE3CFDF7F526F608BD094BC9CAA 1 5 6 4 325 | empathetic BF74AAE3CFDF7F526F608BD094BC9CAA 1 6 8 5 326 | shield BF74AAE3CFDF7F526F608BD094BC9CAA 1 4 9 5 327 | endangerment 328DA17593A61A80331F656D235924D7 1 2 7 1 328 | protection 328DA17593A61A80331F656D235924D7 1 7 4 8 329 | sympathy FCB418018A029FF3B21D7B8AE3E18EB8 1 7 4 9 330 | exploit FCB418018A029FF3B21D7B8AE3E18EB8 1 1 9 1 331 | security FCB418018A029FF3B21D7B8AE3E18EB8 1 9 9 9 332 | killer FCB418018A029FF3B21D7B8AE3E18EB8 1 1 9 1 333 | fight D6F12FBD96E07B40FE03E40D4F91D0C8 1 3 6 5 334 | exploit D6F12FBD96E07B40FE03E40D4F91D0C8 1 5 5 6 335 | defenseless D6F12FBD96E07B40FE03E40D4F91D0C8 1 3 5 9 336 | crusher D6F12FBD96E07B40FE03E40D4F91D0C8 1 2 6 1 337 | cruelty D6F12FBD96E07B40FE03E40D4F91D0C8 1 1 6 1 338 | protector D6F12FBD96E07B40FE03E40D4F91D0C8 1 7 6 9 339 | peaceable D6F12FBD96E07B40FE03E40D4F91D0C8 1 7 5 8 340 | defense 586EF5A47BD329D537DDB3E7D413A871 1 5 5 5 341 | peacetime D6F12FBD96E07B40FE03E40D4F91D0C8 1 8 6 8 342 | security 586EF5A47BD329D537DDB3E7D413A871 1 7 3 7 343 | destroy D6F12FBD96E07B40FE03E40D4F91D0C8 1 1 7 1 344 | compassionate 68C5A2861FEA72A6C6DFA762A62C4594 1 6 7 6 345 | protectionist D6F12FBD96E07B40FE03E40D4F91D0C8 1 6 6 8 346 | cruelty 68C5A2861FEA72A6C6DFA762A62C4594 1 5 7 5 347 | compassionate 586EF5A47BD329D537DDB3E7D413A871 1 7 6 8 348 | detrimental 68C5A2861FEA72A6C6DFA762A62C4594 1 5 8 4 349 | defenestration 68C5A2861FEA72A6C6DFA762A62C4594 1 4 9 6 350 | spurn 68C5A2861FEA72A6C6DFA762A62C4594 1 4 9 5 351 | defendant 586EF5A47BD329D537DDB3E7D413A871 1 3 4 3 352 | brutal 586EF5A47BD329D537DDB3E7D413A871 1 5 5 5 353 | endangered 586EF5A47BD329D537DDB3E7D413A871 1 5 5 5 354 | empathy 586EF5A47BD329D537DDB3E7D413A871 1 7 7 7 355 | safehold 586EF5A47BD329D537DDB3E7D413A871 1 5 5 5 356 | cruelty 586EF5A47BD329D537DDB3E7D413A871 1 4 5 6 357 | protectorship 586EF5A47BD329D537DDB3E7D413A871 1 8 8 9 358 | safe 586EF5A47BD329D537DDB3E7D413A871 1 7 7 8 359 | peacetime 586EF5A47BD329D537DDB3E7D413A871 1 7 7 8 360 | cruelly 586EF5A47BD329D537DDB3E7D413A871 1 4 5 2 361 | exploit 586EF5A47BD329D537DDB3E7D413A871 1 5 5 5 362 | peacekeeping 586EF5A47BD329D537DDB3E7D413A871 1 5 5 7 363 | amity 586EF5A47BD329D537DDB3E7D413A871 1 8 8 8 364 | fight 586EF5A47BD329D537DDB3E7D413A871 1 7 8 8 365 | shield 586EF5A47BD329D537DDB3E7D413A871 1 7 7 7 366 | defenseless 586EF5A47BD329D537DDB3E7D413A871 1 7 7 8 367 | kill 586EF5A47BD329D537DDB3E7D413A871 1 4 5 4 368 | guard 586EF5A47BD329D537DDB3E7D413A871 1 7 8 7 369 | care 15E79E1BDCA40241D5FE6651A2077621 1 9 2 9 370 | cruelty 15E79E1BDCA40241D5FE6651A2077621 1 1 7 1 371 | annihilation 15E79E1BDCA40241D5FE6651A2077621 1 1 8 1 372 | endangerment 15E79E1BDCA40241D5FE6651A2077621 1 3 7 1 373 | protector 15E79E1BDCA40241D5FE6651A2077621 1 7 4 7 374 | sympathize 15E79E1BDCA40241D5FE6651A2077621 1 9 2 9 375 | shield 15E79E1BDCA40241D5FE6651A2077621 1 7 2 9 376 | sympathetic 15E79E1BDCA40241D5FE6651A2077621 1 7 3 7 377 | security 15E79E1BDCA40241D5FE6651A2077621 1 7 1 8 378 | empathetic 15E79E1BDCA40241D5FE6651A2077621 1 9 1 9 379 | peacenik 15E79E1BDCA40241D5FE6651A2077621 1 6 4 8 380 | peace 15E79E1BDCA40241D5FE6651A2077621 1 8 1 9 381 | sympathy 15E79E1BDCA40241D5FE6651A2077621 1 8 2 9 382 | kill 15E79E1BDCA40241D5FE6651A2077621 1 1 9 1 383 | shelter 15E79E1BDCA40241D5FE6651A2077621 1 9 1 9 384 | brutal 15E79E1BDCA40241D5FE6651A2077621 1 2 9 2 385 | exploit 15E79E1BDCA40241D5FE6651A2077621 1 1 9 1 386 | protectorship 15E79E1BDCA40241D5FE6651A2077621 1 8 1 9 387 | safe 3C326DAE2FC36C4BEC292536C1A05813 1 9 1 9 388 | peace 3C326DAE2FC36C4BEC292536C1A05813 1 8 1 8 389 | amity 15E79E1BDCA40241D5FE6651A2077621 1 5 5 5 390 | defenestration 15E79E1BDCA40241D5FE6651A2077621 1 2 7 3 391 | fight 3C326DAE2FC36C4BEC292536C1A05813 1 2 8 1 392 | destroy 3C326DAE2FC36C4BEC292536C1A05813 1 1 8 1 393 | safekeeping 3C326DAE2FC36C4BEC292536C1A05813 1 8 2 9 394 | annihilation FF5D5E71B94719040105E311AF3B7439 1 3 4 3 395 | ravage FF5D5E71B94719040105E311AF3B7439 1 2 9 9 396 | defenestration FF5D5E71B94719040105E311AF3B7439 1 9 9 9 397 | ruin FF5D5E71B94719040105E311AF3B7439 1 2 2 2 398 | defenestration 17B3FBC4AC55D595225F84535BE4F193 1 1 7 1 399 | sympathize 17B3FBC4AC55D595225F84535BE4F193 1 5 5 9 400 | suffer 17B3FBC4AC55D595225F84535BE4F193 1 1 9 1 401 | brutalise 17B3FBC4AC55D595225F84535BE4F193 1 1 8 1 402 | destroy 17B3FBC4AC55D595225F84535BE4F193 1 1 7 1 403 | peacetime 17B3FBC4AC55D595225F84535BE4F193 1 8 1 9 404 | ruin 17B3FBC4AC55D595225F84535BE4F193 1 1 8 1 405 | defenseless 17B3FBC4AC55D595225F84535BE4F193 1 2 1 2 406 | defense A9D582CAF5ADDF90115AD440F381BFE1 1 8 2 8 407 | stomp BD85F3D6AEB67D15AC6E1AE8B35E574B 1 1 5 1 408 | defenseless A9D582CAF5ADDF90115AD440F381BFE1 1 1 5 1 409 | ravage 5D5C6700B1DBCAA61E9AAE0AF7412A4D 1 2 7 1 410 | empathy BD85F3D6AEB67D15AC6E1AE8B35E574B 1 7 1 9 411 | defense 17B3FBC4AC55D595225F84535BE4F193 1 5 5 1 412 | spurn 5D5C6700B1DBCAA61E9AAE0AF7412A4D 1 4 7 4 413 | guard 17B3FBC4AC55D595225F84535BE4F193 1 5 5 5 414 | peaceable A9D582CAF5ADDF90115AD440F381BFE1 1 8 1 9 415 | annihilation BD85F3D6AEB67D15AC6E1AE8B35E574B 1 1 5 1 416 | empathy 5D5C6700B1DBCAA61E9AAE0AF7412A4D 1 5 1 9 417 | empathy A9D582CAF5ADDF90115AD440F381BFE1 1 9 1 9 418 | shield BD85F3D6AEB67D15AC6E1AE8B35E574B 1 5 5 7 419 | sympathetic 5D5C6700B1DBCAA61E9AAE0AF7412A4D 1 2 3 9 420 | annihilation 361B5614BCA7387EAC0939436228E942 1 1 5 1 421 | killer BD85F3D6AEB67D15AC6E1AE8B35E574B 1 1 5 1 422 | peaceable 5D5C6700B1DBCAA61E9AAE0AF7412A4D 1 7 4 8 423 | protector 361B5614BCA7387EAC0939436228E942 1 9 9 9 424 | peaceable 361B5614BCA7387EAC0939436228E942 1 9 9 9 425 | shield 4E0C798A73F016100353720FAA644B12 1 5 5 8 426 | protector 4E0C798A73F016100353720FAA644B12 1 7 5 8 427 | peacemaker 4E0C798A73F016100353720FAA644B12 1 7 3 8 428 | spurn 361B5614BCA7387EAC0939436228E942 1 5 6 3 429 | peacemaker 361B5614BCA7387EAC0939436228E942 1 9 9 9 430 | brutalise 4E0C798A73F016100353720FAA644B12 1 2 6 1 431 | stomp 4E0C798A73F016100353720FAA644B12 1 5 5 3 432 | safe 4E0C798A73F016100353720FAA644B12 1 7 2 8 433 | endangered 4E0C798A73F016100353720FAA644B12 1 3 5 2 434 | compassionate 4E0C798A73F016100353720FAA644B12 1 8 2 8 435 | defense 4E0C798A73F016100353720FAA644B12 1 5 5 6 436 | exploit 4E0C798A73F016100353720FAA644B12 1 3 6 2 437 | killer 382235231A1554D2CF102326A186B317 1 2 8 1 438 | suffer 382235231A1554D2CF102326A186B317 1 2 5 2 439 | peacemaker 382235231A1554D2CF102326A186B317 1 8 7 9 440 | peacenik 382235231A1554D2CF102326A186B317 1 8 6 2 441 | protectionism 382235231A1554D2CF102326A186B317 1 8 7 8 442 | compassionate 382235231A1554D2CF102326A186B317 1 8 3 9 443 | sympathy 382235231A1554D2CF102326A186B317 1 3 3 8 444 | safe 382235231A1554D2CF102326A186B317 1 9 8 9 445 | protectionism 5F37895381928F9A06737AF25F2EF2A0 1 7 7 9 446 | suffer 5F37895381928F9A06737AF25F2EF2A0 1 1 5 1 447 | sympathy 4E0C798A73F016100353720FAA644B12 1 6 3 9 448 | suffer 4E0C798A73F016100353720FAA644B12 1 2 5 3 449 | protectionism 4E0C798A73F016100353720FAA644B12 1 5 5 7 450 | sympathy 0745BC854FB0592A3BEB647397F12DB2 1 7 3 7 451 | protectionism 2D7D894855AD47A43EC14850C4E561A7 1 5 5 5 452 | peacenik 38E3D8591547553DBF27D930293B5BBB 1 2 5 7 -------------------------------------------------------------------------------- /moralstrength_raw/RAW_ANNOTATIONS/filtered_annotators/RAW_ANNOTATIONS_FAIRNESS.txt: -------------------------------------------------------------------------------- 1 | WORD ANNOTATOR_ID RELEVANCE VALENCE AROUSAL EXPRESSED_MORAL 2 | competent D6180738F37A31312E820B069C47C3C0 2 7 7 3 3 | wrongfully D6180738F37A31312E820B069C47C3C0 1 2 3 4 4 | slavery D6180738F37A31312E820B069C47C3C0 1 5 2 5 5 | disproportionately D6180738F37A31312E820B069C47C3C0 1 5 5 4 6 | impartial D6180738F37A31312E820B069C47C3C0 1 7 6 9 7 | confounding D6180738F37A31312E820B069C47C3C0 2 4 5 5 8 | bigotry D6180738F37A31312E820B069C47C3C0 1 6 2 4 9 | relatively D6180738F37A31312E820B069C47C3C0 1 6 6 5 10 | inhumane D6180738F37A31312E820B069C47C3C0 1 2 7 2 11 | proportionality D6180738F37A31312E820B069C47C3C0 1 5 2 1 12 | consumerism D6180738F37A31312E820B069C47C3C0 1 6 6 4 13 | accepting D6180738F37A31312E820B069C47C3C0 1 4 6 7 14 | permanency 8F19CDD26BDBD0605677A832E6EF7B80 1 5 5 7 15 | honest 8F19CDD26BDBD0605677A832E6EF7B80 1 9 1 9 16 | proportionality 8F19CDD26BDBD0605677A832E6EF7B80 2 5 4 4 17 | impartial E6F53B54A4814DA2614922482CD00894 1 8 5 9 18 | homophobic E6F53B54A4814DA2614922482CD00894 2 1 2 1 19 | permanency E6F53B54A4814DA2614922482CD00894 1 7 3 5 20 | accepting E6F53B54A4814DA2614922482CD00894 1 8 2 8 21 | oppression E6F53B54A4814DA2614922482CD00894 2 1 5 1 22 | subjective E6F53B54A4814DA2614922482CD00894 2 5 6 2 23 | inhumane E6F53B54A4814DA2614922482CD00894 2 1 6 1 24 | inaccurate E6F53B54A4814DA2614922482CD00894 2 2 5 2 25 | wrongfully E6F53B54A4814DA2614922482CD00894 2 2 6 2 26 | bigotry E6F53B54A4814DA2614922482CD00894 2 1 7 1 27 | honest E6F53B54A4814DA2614922482CD00894 1 8 6 9 28 | competent E6F53B54A4814DA2614922482CD00894 1 7 5 9 29 | inhumane CCCE000DABB136037FB67740E90F1EE7 1 3 5 6 30 | subjective CCCE000DABB136037FB67740E90F1EE7 1 5 4 5 31 | impartial CCCE000DABB136037FB67740E90F1EE7 1 6 4 8 32 | consumerism CCCE000DABB136037FB67740E90F1EE7 1 3 5 3 33 | wrongfully CCCE000DABB136037FB67740E90F1EE7 1 3 4 8 34 | competent CCCE000DABB136037FB67740E90F1EE7 1 5 5 4 35 | permanency CCCE000DABB136037FB67740E90F1EE7 1 5 5 4 36 | disproportionately CCCE000DABB136037FB67740E90F1EE7 1 4 5 3 37 | slavery CCCE000DABB136037FB67740E90F1EE7 1 1 7 2 38 | relatively CCCE000DABB136037FB67740E90F1EE7 1 5 5 6 39 | inaccurate CCCE000DABB136037FB67740E90F1EE7 1 4 5 6 40 | confounding CCCE000DABB136037FB67740E90F1EE7 2 5 5 5 41 | disproportionately B778FDC5991619C6D86F71BA87A95FE6 1 2 2 1 42 | impartial B778FDC5991619C6D86F71BA87A95FE6 1 7 2 9 43 | bigotry B778FDC5991619C6D86F71BA87A95FE6 1 1 5 9 44 | impartial 408A5456560E6B6AC49B24B3E1A8C4F5 1 2 2 5 45 | bigotry 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 46 | proportionality 408A5456560E6B6AC49B24B3E1A8C4F5 1 6 5 5 47 | wrongfully 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 48 | amplitude 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 49 | honest 408A5456560E6B6AC49B24B3E1A8C4F5 1 9 9 9 50 | homophobic 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 51 | inaccurate 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 52 | permanency 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 53 | slavery 09E56CFB89A8A152D2997CA77B0483B1 1 2 8 2 54 | honest 09E56CFB89A8A152D2997CA77B0483B1 1 7 4 8 55 | inaccurate 09E56CFB89A8A152D2997CA77B0483B1 1 3 7 3 56 | slavery 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 57 | consumerism 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 58 | competent 408A5456560E6B6AC49B24B3E1A8C4F5 1 5 5 5 59 | homophobic 09E56CFB89A8A152D2997CA77B0483B1 1 1 7 4 60 | wrongfully 09E56CFB89A8A152D2997CA77B0483B1 1 3 7 3 61 | subjective 09E56CFB89A8A152D2997CA77B0483B1 1 6 4 3 62 | disproportionately 09E56CFB89A8A152D2997CA77B0483B1 1 3 7 1 63 | consumerism 09E56CFB89A8A152D2997CA77B0483B1 1 5 6 3 64 | accepting 09E56CFB89A8A152D2997CA77B0483B1 1 5 6 7 65 | oppression 09E56CFB89A8A152D2997CA77B0483B1 1 1 8 3 66 | amplitude 09E56CFB89A8A152D2997CA77B0483B1 1 5 5 2 67 | competent 09E56CFB89A8A152D2997CA77B0483B1 1 6 6 7 68 | amplitude 185F6108754E47F75296A701F68364BF 1 6 7 6 69 | homophobic 185F6108754E47F75296A701F68364BF 1 4 6 5 70 | determinant 185F6108754E47F75296A701F68364BF 1 6 6 4 71 | oppression DFD4A3D0BD652239CB642605673A0630 1 2 2 4 72 | inaccurate DFD4A3D0BD652239CB642605673A0630 1 1 1 1 73 | accepting DFD4A3D0BD652239CB642605673A0630 1 9 9 9 74 | confounding DFD4A3D0BD652239CB642605673A0630 1 5 5 5 75 | inhumane DFD4A3D0BD652239CB642605673A0630 1 1 5 1 76 | consumerism DFD4A3D0BD652239CB642605673A0630 1 5 5 9 77 | honest DFD4A3D0BD652239CB642605673A0630 1 9 5 9 78 | bigotry DFD4A3D0BD652239CB642605673A0630 1 1 5 1 79 | amplitude DFD4A3D0BD652239CB642605673A0630 1 5 5 5 80 | homophobic DFD4A3D0BD652239CB642605673A0630 1 3 5 1 81 | proportionality DFD4A3D0BD652239CB642605673A0630 1 5 5 1 82 | slavery DFD4A3D0BD652239CB642605673A0630 1 1 5 1 83 | homophobic 2568370CAB968D0C4B6B1F6757FAC0BD 1 4 5 3 84 | honest 2568370CAB968D0C4B6B1F6757FAC0BD 1 5 5 6 85 | homophobic BD42B12CAFB6BEFEF1673E12A6561B84 2 5 2 5 86 | oppression CDE04327B00D294A0EF8E1F83B5905B4 1 5 5 7 87 | determinant CDE04327B00D294A0EF8E1F83B5905B4 1 5 5 8 88 | relatively CDE04327B00D294A0EF8E1F83B5905B4 2 6 5 5 89 | relatively 9ADD0ACCFEDEACE5717A3262B7BF41FB 1 5 4 5 90 | permanency 9ADD0ACCFEDEACE5717A3262B7BF41FB 1 6 3 3 91 | subjective 9ADD0ACCFEDEACE5717A3262B7BF41FB 1 4 2 7 92 | justify 586EF5A47BD329D537DDB3E7D413A871 1 8 6 5 93 | unequal 586EF5A47BD329D537DDB3E7D413A871 1 3 5 6 94 | segregation 586EF5A47BD329D537DDB3E7D413A871 1 5 8 5 95 | equal C481AC42604F49AD60E147E0D48DA9A8 1 5 6 9 96 | constant 315875F669D472421C781AD1E2E6B787 1 6 6 5 97 | justification 315875F669D472421C781AD1E2E6B787 1 6 7 5 98 | segregationism C481AC42604F49AD60E147E0D48DA9A8 1 1 4 1 99 | prejudice 315875F669D472421C781AD1E2E6B787 1 6 6 5 100 | prejudicial 315875F669D472421C781AD1E2E6B787 1 6 6 5 101 | discriminatory C481AC42604F49AD60E147E0D48DA9A8 1 1 4 1 102 | justify 315875F669D472421C781AD1E2E6B787 1 6 6 5 103 | equal 315875F669D472421C781AD1E2E6B787 1 6 6 5 104 | unscrupulous C481AC42604F49AD60E147E0D48DA9A8 1 1 4 3 105 | unequalled 315875F669D472421C781AD1E2E6B787 1 5 5 5 106 | disproportionate 315875F669D472421C781AD1E2E6B787 1 5 4 5 107 | homologous 315875F669D472421C781AD1E2E6B787 1 4 4 4 108 | equaliser 8FB661F85CEA9FF87506002B3BFD078E 1 7 7 7 109 | exclusion C481AC42604F49AD60E147E0D48DA9A8 1 1 6 1 110 | fair-minded 8FB661F85CEA9FF87506002B3BFD078E 1 8 8 9 111 | homologous C481AC42604F49AD60E147E0D48DA9A8 1 5 5 1 112 | equalitarian C481AC42604F49AD60E147E0D48DA9A8 1 3 7 5 113 | disproportionate 8FB661F85CEA9FF87506002B3BFD078E 1 4 5 3 114 | discrimination BA45B96CDD19418387D57EEE75A1F127 1 1 8 1 115 | unprejudiced C481AC42604F49AD60E147E0D48DA9A8 1 6 7 7 116 | unprejudiced BA45B96CDD19418387D57EEE75A1F127 1 9 2 9 117 | fair 8FB661F85CEA9FF87506002B3BFD078E 1 8 8 9 118 | tolerant BA45B96CDD19418387D57EEE75A1F127 1 9 2 9 119 | unequalled 15E79E1BDCA40241D5FE6651A2077621 1 5 6 3 120 | prejudicial 15E79E1BDCA40241D5FE6651A2077621 1 1 6 8 121 | unequal BA45B96CDD19418387D57EEE75A1F127 1 1 8 9 122 | prejudice 8FB661F85CEA9FF87506002B3BFD078E 1 4 5 2 123 | preference 15E79E1BDCA40241D5FE6651A2077621 1 2 4 9 124 | disproportion 15E79E1BDCA40241D5FE6651A2077621 1 2 6 1 125 | unprejudiced 8FB661F85CEA9FF87506002B3BFD078E 1 7 8 8 126 | discriminatory 15E79E1BDCA40241D5FE6651A2077621 1 1 6 9 127 | equable 8FB661F85CEA9FF87506002B3BFD078E 1 7 6 6 128 | unscrupulous BA45B96CDD19418387D57EEE75A1F127 1 1 8 1 129 | discrimination 8FB661F85CEA9FF87506002B3BFD078E 1 4 5 2 130 | unjust 15E79E1BDCA40241D5FE6651A2077621 1 4 8 9 131 | injustice 15E79E1BDCA40241D5FE6651A2077621 1 1 8 9 132 | equalitarian AB3668D1FB431F6CE11568AB689C58C2 1 8 1 1 133 | justification AB3668D1FB431F6CE11568AB689C58C2 1 6 3 9 134 | prejudicial AB3668D1FB431F6CE11568AB689C58C2 1 2 6 8 135 | bigot AB3668D1FB431F6CE11568AB689C58C2 1 8 3 2 136 | equalitarian 4E0C798A73F016100353720FAA644B12 1 7 3 7 137 | equivalent AB3668D1FB431F6CE11568AB689C58C2 1 6 3 1 138 | justice 4E0C798A73F016100353720FAA644B12 1 7 2 8 139 | fair-minded AB3668D1FB431F6CE11568AB689C58C2 1 8 1 9 140 | fair-minded D4633A0AC6ED3DD59D88AC6524AFEE1A 1 7 6 8 141 | reasonable AB3668D1FB431F6CE11568AB689C58C2 1 8 3 1 142 | balance AB3668D1FB431F6CE11568AB689C58C2 1 8 2 1 143 | discrimination 4E0C798A73F016100353720FAA644B12 1 4 5 4 144 | unjust AB3668D1FB431F6CE11568AB689C58C2 1 1 7 9 145 | justificatory D4633A0AC6ED3DD59D88AC6524AFEE1A 1 3 4 3 146 | bigot 4E0C798A73F016100353720FAA644B12 1 2 2 5 147 | unjust D4633A0AC6ED3DD59D88AC6524AFEE1A 1 3 2 7 148 | unscrupulous 4E0C798A73F016100353720FAA644B12 1 3 5 5 149 | equal 4E0C798A73F016100353720FAA644B12 1 7 6 7 150 | egalitarian D4633A0AC6ED3DD59D88AC6524AFEE1A 1 6 5 7 151 | reciprocal 586EF5A47BD329D537DDB3E7D413A871 1 7 7 9 152 | unjustified 4E0C798A73F016100353720FAA644B12 1 5 5 5 153 | injustice 4E0C798A73F016100353720FAA644B12 1 3 5 7 154 | preference D4633A0AC6ED3DD59D88AC6524AFEE1A 1 5 5 5 155 | discriminatory 4E0C798A73F016100353720FAA644B12 1 4 4 4 156 | equity 586EF5A47BD329D537DDB3E7D413A871 1 9 5 9 157 | dishonest D4633A0AC6ED3DD59D88AC6524AFEE1A 1 1 4 3 158 | disproportionate 586EF5A47BD329D537DDB3E7D413A871 1 1 4 1 159 | exclude 586EF5A47BD329D537DDB3E7D413A871 1 4 4 2 160 | justice D4633A0AC6ED3DD59D88AC6524AFEE1A 1 7 6 9 161 | bigot D4633A0AC6ED3DD59D88AC6524AFEE1A 1 5 5 3 162 | equable D4633A0AC6ED3DD59D88AC6524AFEE1A 1 5 5 4 163 | injustice 4535A918C5EBDAA4929D32A143E0B1C2 1 3 5 5 164 | equal 4535A918C5EBDAA4929D32A143E0B1C2 1 7 6 7 165 | balance 4535A918C5EBDAA4929D32A143E0B1C2 1 7 2 8 166 | segregationist 4535A918C5EBDAA4929D32A143E0B1C2 1 5 5 3 167 | unjust 4535A918C5EBDAA4929D32A143E0B1C2 1 3 8 3 168 | discriminatory B778FDC5991619C6D86F71BA87A95FE6 1 4 2 1 169 | justice B778FDC5991619C6D86F71BA87A95FE6 1 7 2 9 170 | constant B778FDC5991619C6D86F71BA87A95FE6 1 6 2 3 171 | reciprocal 4535A918C5EBDAA4929D32A143E0B1C2 1 7 6 8 172 | reciprocal B778FDC5991619C6D86F71BA87A95FE6 1 6 3 7 173 | equaliser 4535A918C5EBDAA4929D32A143E0B1C2 1 7 7 7 174 | honesty B778FDC5991619C6D86F71BA87A95FE6 1 7 2 8 175 | right FB73B91F87BE6E8EA3E745DDA7473E2B 1 7 2 6 176 | equity B778FDC5991619C6D86F71BA87A95FE6 1 8 2 9 177 | unfair FB73B91F87BE6E8EA3E745DDA7473E2B 1 3 6 9 178 | discrimination 4535A918C5EBDAA4929D32A143E0B1C2 1 3 7 2 179 | egalitarian FB73B91F87BE6E8EA3E745DDA7473E2B 1 5 3 2 180 | segregationism B778FDC5991619C6D86F71BA87A95FE6 1 2 4 1 181 | fair-mindedness B778FDC5991619C6D86F71BA87A95FE6 1 8 2 9 182 | unjust FB73B91F87BE6E8EA3E745DDA7473E2B 1 1 7 2 183 | justify FB73B91F87BE6E8EA3E745DDA7473E2B 1 7 4 1 184 | unprejudiced FB73B91F87BE6E8EA3E745DDA7473E2B 1 9 3 9 185 | fair-mindedness FB73B91F87BE6E8EA3E745DDA7473E2B 1 8 5 9 186 | unjustified FB73B91F87BE6E8EA3E745DDA7473E2B 1 5 6 5 187 | dissociate D8F673A7AE6DF6AE253D31E038E1C8F7 1 3 3 1 188 | unjustified D8F673A7AE6DF6AE253D31E038E1C8F7 1 2 5 7 189 | discriminatory D8F673A7AE6DF6AE253D31E038E1C8F7 1 2 7 5 190 | prejudgment D531FA7EA3FA78411003699A7D7D4C3B 1 4 5 4 191 | equable D8F673A7AE6DF6AE253D31E038E1C8F7 1 6 5 3 192 | balance D8F673A7AE6DF6AE253D31E038E1C8F7 1 7 2 9 193 | exclude D8F673A7AE6DF6AE253D31E038E1C8F7 1 4 6 4 194 | evenness D8F673A7AE6DF6AE253D31E038E1C8F7 1 8 5 7 195 | equaliser 3356A89117EE8BAFA7D736A5E4965E7D 1 5 3 9 196 | reasonable 3356A89117EE8BAFA7D736A5E4965E7D 1 9 1 2 197 | fair 3356A89117EE8BAFA7D736A5E4965E7D 1 7 2 9 198 | unequalled 3356A89117EE8BAFA7D736A5E4965E7D 1 1 3 5 199 | reciprocal 3356A89117EE8BAFA7D736A5E4965E7D 1 5 5 5 200 | bias 3356A89117EE8BAFA7D736A5E4965E7D 1 5 5 5 201 | constant 3356A89117EE8BAFA7D736A5E4965E7D 1 5 5 5 202 | disproportionate 3356A89117EE8BAFA7D736A5E4965E7D 1 1 1 7 203 | prejudgment 3356A89117EE8BAFA7D736A5E4965E7D 1 5 5 5 204 | exclusion 133A7FC3883AA857DEC2FE736035F72E 1 3 5 2 205 | fair 133A7FC3883AA857DEC2FE736035F72E 1 6 4 9 206 | segregationism 133A7FC3883AA857DEC2FE736035F72E 1 1 5 1 207 | prejudicial 133A7FC3883AA857DEC2FE736035F72E 1 1 5 1 208 | unprejudiced 133A7FC3883AA857DEC2FE736035F72E 1 7 4 8 209 | unfair 133A7FC3883AA857DEC2FE736035F72E 1 3 6 3 210 | equalitarian 133A7FC3883AA857DEC2FE736035F72E 1 8 7 9 211 | dishonest 133A7FC3883AA857DEC2FE736035F72E 1 2 6 3 212 | constant 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 6 5 213 | fair 50F1D17435B53F4BE0F5D1F47C8D7826 1 7 7 9 214 | unequalised 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 5 2 215 | right 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 5 7 216 | justification 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 5 6 217 | prejudgment 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 5 3 218 | segregationism 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 5 3 219 | bigot 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 5 3 220 | unbiased 50F1D17435B53F4BE0F5D1F47C8D7826 1 5 5 8 221 | disproportion 4EAD4A593DD30560FFF6BB3F5E66800B 1 3 3 6 222 | justify 4EAD4A593DD30560FFF6BB3F5E66800B 1 6 6 8 223 | equivalent 4EAD4A593DD30560FFF6BB3F5E66800B 1 5 4 4 224 | unequalled 4EAD4A593DD30560FFF6BB3F5E66800B 1 4 4 3 225 | exclusion 9C2E01F223AE7768BC0D9843A50A88A5 1 3 3 8 226 | prejudice 4EAD4A593DD30560FFF6BB3F5E66800B 1 2 2 7 227 | discrimination 9C2E01F223AE7768BC0D9843A50A88A5 1 3 4 3 228 | exclude 9C2E01F223AE7768BC0D9843A50A88A5 1 4 4 3 229 | egalitarian 9C2E01F223AE7768BC0D9843A50A88A5 1 7 6 9 230 | fair-minded 4EAD4A593DD30560FFF6BB3F5E66800B 1 6 4 8 231 | dishonest 9C2E01F223AE7768BC0D9843A50A88A5 1 3 4 3 232 | discrimination 4EAD4A593DD30560FFF6BB3F5E66800B 1 4 3 8 233 | bias 4EAD4A593DD30560FFF6BB3F5E66800B 1 3 5 8 234 | exclude 4EAD4A593DD30560FFF6BB3F5E66800B 1 3 5 7 235 | dissociate CB72D1B4E93AF561AC69D87B79B22F1C 1 5 5 5 236 | segregationism CB72D1B4E93AF561AC69D87B79B22F1C 1 5 6 5 237 | equaliser CB72D1B4E93AF561AC69D87B79B22F1C 1 5 6 6 238 | unequalled CB72D1B4E93AF561AC69D87B79B22F1C 1 5 5 5 239 | prejudgment CB72D1B4E93AF561AC69D87B79B22F1C 1 5 5 3 240 | egalitarian CB72D1B4E93AF561AC69D87B79B22F1C 1 5 5 7 241 | unequal C01309EFB777076FE6AD43225A49D457 1 5 5 3 242 | disproportion C01309EFB777076FE6AD43225A49D457 1 5 5 2 243 | disproportion CB72D1B4E93AF561AC69D87B79B22F1C 1 5 5 4 244 | exclusion CB72D1B4E93AF561AC69D87B79B22F1C 1 5 5 3 245 | unfair CB72D1B4E93AF561AC69D87B79B22F1C 1 5 5 2 246 | equable 9C2E01F223AE7768BC0D9843A50A88A5 1 4 6 9 247 | unjustified 9C2E01F223AE7768BC0D9843A50A88A5 1 3 7 6 248 | segregationist C01309EFB777076FE6AD43225A49D457 1 5 5 4 249 | unequal 9C2E01F223AE7768BC0D9843A50A88A5 1 4 5 7 250 | justify C01309EFB777076FE6AD43225A49D457 1 6 6 8 251 | bigot 9C2E01F223AE7768BC0D9843A50A88A5 1 3 7 5 252 | justification C01309EFB777076FE6AD43225A49D457 1 5 5 8 253 | unfair C01309EFB777076FE6AD43225A49D457 1 4 5 8 254 | reasonable C01309EFB777076FE6AD43225A49D457 1 5 5 5 255 | disproportionate C01309EFB777076FE6AD43225A49D457 1 5 5 2 256 | dissociate FDEDCF7929B94077E133985905041C7C 1 5 1 2 257 | justice FDEDCF7929B94077E133985905041C7C 1 5 3 3 258 | bias FDEDCF7929B94077E133985905041C7C 1 1 4 5 259 | fair-mindedness FDEDCF7929B94077E133985905041C7C 1 8 4 9 260 | unscrupulous FDEDCF7929B94077E133985905041C7C 1 1 6 1 261 | unjustified FDEDCF7929B94077E133985905041C7C 1 1 3 1 262 | segregationist FDEDCF7929B94077E133985905041C7C 1 1 7 1 263 | equivalent FDEDCF7929B94077E133985905041C7C 1 5 2 8 264 | equable FDEDCF7929B94077E133985905041C7C 1 7 2 9 265 | prejudgment ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 3 8 4 266 | unfair ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 1 9 1 267 | unbiased ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 9 9 9 268 | tolerant ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 4 6 8 269 | right ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 9 7 9 270 | evenness ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 8 8 7 271 | segregation ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 4 7 9 272 | honesty ACDD6FA536A0E1CE6C65A3F5687F3BAC 1 9 9 9 273 | unbiased 208CA3F1150088304C597D9E4B2AB24A 1 8 8 8 274 | segregationist 208CA3F1150088304C597D9E4B2AB24A 1 2 4 2 275 | honesty 208CA3F1150088304C597D9E4B2AB24A 1 9 9 9 276 | equalitarian 208CA3F1150088304C597D9E4B2AB24A 1 9 8 9 277 | reasonable 208CA3F1150088304C597D9E4B2AB24A 1 8 9 8 278 | constant 208CA3F1150088304C597D9E4B2AB24A 1 5 5 5 279 | prejudice 208CA3F1150088304C597D9E4B2AB24A 1 1 4 1 280 | right 208CA3F1150088304C597D9E4B2AB24A 1 9 9 9 281 | justificatory 208CA3F1150088304C597D9E4B2AB24A 1 7 6 8 -------------------------------------------------------------------------------- /moralstrength_raw/tasks/Editor Preview of Task — Authority.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oaraque/moral-foundations/2be815db653ec769587e53eb46557b7bfc87058a/moralstrength_raw/tasks/Editor Preview of Task — Authority.pdf -------------------------------------------------------------------------------- /moralstrength_raw/tasks/Editor Preview of Task — Care.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oaraque/moral-foundations/2be815db653ec769587e53eb46557b7bfc87058a/moralstrength_raw/tasks/Editor Preview of Task — Care.pdf 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https://raw.githubusercontent.com/oaraque/moral-foundations/2be815db653ec769587e53eb46557b7bfc87058a/moralstrength_raw/tasks/Editor Preview of Task — Purity.pdf -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | spacy 2 | pandas 3 | gsitk 4 | numpy 5 | scikit_learn 6 | -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | description-file = README.md -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import io 2 | import setuptools 3 | 4 | VERSION = '0.2.13' 5 | 6 | with open("README.md", "r") as fh: 7 | long_description = fh.read() 8 | 9 | def parse_requirements(filename): 10 | """ load requirements from a pip requirements file """ 11 | with io.open(filename, 'r') as f: 12 | lineiter = list(line.strip() for line in f) 13 | return [line for line in lineiter if line and not line.startswith("#")] 14 | 15 | install_reqs = parse_requirements("requirements.txt") 16 | 17 | setuptools.setup( 18 | name='moralstrength', 19 | packages=['moralstrength'], 20 | version=VERSION, 21 | #scripts=['moralstrength', 'data', 'estimators', 'lexicon_use', 'moralstrengthdict'] , 22 | author="Oscar Araque, Lorenzo Gatti and Kyriaki Kalimeri", 23 | author_email="o.araque@upm.es", 24 | description="A package to predict the Moral Foundations for a tweet or text", 25 | long_description=long_description, 26 | long_description_content_type="text/markdown", 27 | url="https://github.com/oaraque/moral-foundations/", 28 | download_url='https://github.com/oaraque/moral-foundations/tarball/{}'.format(VERSION), 29 | license='LGPLv3', 30 | classifiers=[ 31 | "Intended Audience :: Science/Research", 32 | "Natural Language :: English", 33 | "Topic :: Text Processing :: Linguistic", 34 | "Programming Language :: Python :: 3", 35 | "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)", 36 | "Operating System :: OS Independent", 37 | ], 38 | keywords=['moral foundations', 'NLP', 'moralstrength', 'machine learning'], 39 | install_requires=install_reqs, 40 | include_package_data=True, 41 | ) 42 | -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oaraque/moral-foundations/2be815db653ec769587e53eb46557b7bfc87058a/tests/__init__.py -------------------------------------------------------------------------------- /tests/lexicon_use_test.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | from moralstrength import lexicon_use 3 | 4 | def _check_lexicon_size(lex): 5 | return [len(lex[moral].keys()) for moral in lex.keys()] 6 | 7 | original_sizes = _check_lexicon_size(lexicon_use.moral_lex) 8 | new_sizes = _check_lexicon_size(lexicon_use.new_moral_lex) 9 | 10 | def test_default_lexicon_selected(): 11 | # default lexicon is latest version 12 | assert new_sizes == _check_lexicon_size(lexicon_use.current_moral_lex) 13 | 14 | def test_select_version(): 15 | # change to original lexicon 16 | lexicon_use.select_version('original') 17 | assert original_sizes == _check_lexicon_size(lexicon_use.current_moral_lex) 18 | 19 | # change back to latest version 20 | lexicon_use.select_version('latest') 21 | assert new_sizes == _check_lexicon_size(lexicon_use.current_moral_lex) 22 | 23 | # and again to original, just to be sure 24 | lexicon_use.select_version('original') 25 | assert original_sizes == _check_lexicon_size(lexicon_use.current_moral_lex) 26 | -------------------------------------------------------------------------------- /tests/moralstrength_test.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import numpy as np 3 | import pandas as pd 4 | 5 | from moralstrength import ( 6 | get_available_models, get_available_lexicon_traits, get_available_prediction_traits, lexicon_morals, string_moral_values, string_moral_value, word_moral_value, word_moral_annotations, string_vader_moral, string_average_moral, texts_moral, texts_morals, estimate_morals 7 | ) 8 | 9 | 10 | @pytest.fixture 11 | def example_texts(): 12 | with open('tests/test_examples.txt') as f: 13 | text = f.readlines() 14 | text = [t.strip() for t in text] 15 | return text 16 | 17 | def check_prediction(value): 18 | assert value is not None 19 | assert isinstance(value, float) 20 | assert value >= 0 21 | assert value <= 1 22 | 23 | def check_moral_value(value): 24 | assert value is not None 25 | assert isinstance(value, float) or isinstance(value, int) 26 | assert ((value >= 0) and (value <= 9) ) or np.isnan(value) 27 | 28 | def check_moral_polarity(value): 29 | assert value is not None 30 | assert isinstance(value, float) or isinstance(value, int) 31 | assert ((value >= -1) and (value <= 1) ) or np.isnan(value) 32 | 33 | def check_trait_list(result): 34 | assert result is not None 35 | assert isinstance(result, list) 36 | assert len(result) > 0 37 | for element in result: 38 | assert element is not None 39 | assert isinstance(element, str) 40 | 41 | def check_estimation_matrix(result, size): 42 | assert result.shape[0] > 0 43 | assert result.shape[0] == size[0] 44 | assert result.shape[1] > 0 45 | assert result.shape[1] == size[1] 46 | 47 | def test_get_available_models(): 48 | result = get_available_models() 49 | assert result is not None 50 | assert isinstance(result, tuple) 51 | assert len(result) > 0 52 | for element in result: 53 | assert element is not None 54 | assert isinstance(element, str) 55 | 56 | def test_get_available_lexicon_traits(): 57 | result = get_available_lexicon_traits() 58 | check_trait_list(result) 59 | 60 | def test_get_available_prediction_traits(): 61 | result = get_available_prediction_traits() 62 | check_trait_list(result) 63 | 64 | def test_lexicon_morals(): 65 | result = lexicon_morals() 66 | check_trait_list(result) 67 | 68 | def test_string_moral_value(): 69 | mytext = "My cat is loyal only to me." 70 | for moral_trait in get_available_prediction_traits(): 71 | result = string_moral_value(mytext, moral_trait) 72 | check_prediction(result) 73 | 74 | def test_string_moral_values(): 75 | mytext = "My cat is loyal only to me." 76 | result = string_moral_values(mytext) 77 | assert isinstance(result, dict) 78 | for key, value in result.items(): 79 | assert key is not None 80 | assert isinstance(key, str) 81 | check_prediction(value) 82 | 83 | def test_word_moral_value(): 84 | words = ('loyalty', 'cat', 'dog', 'happiness', 'asdfg') 85 | for word in words: 86 | for moral_trait in get_available_lexicon_traits(): 87 | result = word_moral_value(word, moral_trait) 88 | check_moral_value(result) 89 | 90 | def test_word_moral_annotations(): 91 | words = ('loyalty', 'cat', 'dog', 'happiness', 'asdfg') 92 | for word in words: 93 | result = word_moral_annotations(word) 94 | assert isinstance(result, dict) 95 | for key, value in result.items(): 96 | assert key is not None 97 | assert isinstance(key, str) 98 | check_moral_value(value) 99 | 100 | def test_string_average_moral(): 101 | text = "My cat is loyal only to me." 102 | for moral_trait in get_available_lexicon_traits(): 103 | result = string_average_moral(text, moral_trait) 104 | assert isinstance(result, float) 105 | 106 | def test_string_vader_moral(): 107 | text = "My cat is loyal only to me." 108 | for moral_trait in get_available_lexicon_traits(): 109 | result = string_vader_moral(text, moral_trait) 110 | assert isinstance(result, float) 111 | 112 | def test_texts_moral(example_texts): 113 | for moral_trait in lexicon_morals(): 114 | result = texts_moral(example_texts, moral_trait, process=True) 115 | assert len(result) is not 0 116 | assert len(result) == len(example_texts) 117 | for moral_trait_estimation in result: 118 | check_moral_value(moral_trait_estimation) 119 | 120 | def test_texts_morals(example_texts): 121 | result = texts_morals(example_texts, process=True) 122 | assert isinstance(result, np.ndarray) 123 | check_estimation_matrix(result, (len(example_texts), len(lexicon_morals()))) 124 | for doc in result: 125 | for doc_pred in doc: 126 | check_moral_value(doc_pred) 127 | 128 | def test_estimate_morals(example_texts): 129 | result = estimate_morals(example_texts, process=True) 130 | assert isinstance(result, pd.DataFrame) 131 | check_estimation_matrix(result, (len(example_texts), len(lexicon_morals()))) 132 | for doc in result[lexicon_morals()].values: 133 | for doc_pred in doc: 134 | check_moral_value(doc_pred) 135 | -------------------------------------------------------------------------------- /tests/test_examples.txt: -------------------------------------------------------------------------------- 1 | My dog is very loyal to me. 2 | My cat is not loyal, but understands my authority. 3 | He did not want to break the router, he was fixing it. 4 | It is not fair! She cheated on the exams. 5 | Are you pure of heart? Because I am sure not. 6 | Will you take care of me? I am sad. 7 | --------------------------------------------------------------------------------