├── LICENSE.txt ├── README.md └── json ├── emotion-detection-dev.json ├── emotion-detection-trn.json └── emotion-detection-tst.json /LICENSE.txt: -------------------------------------------------------------------------------- 1 | Copyright 2017, Emory University 2 | 3 | Licensed under the Apache License, Version 2.0 (the "License"); 4 | you may not use this file except in compliance with the License. 5 | You may obtain a copy of the License at 6 | 7 | http://www.apache.org/licenses/LICENSE-2.0 8 | 9 | Unless required by applicable law or agreed to in writing, software 10 | distributed under the License is distributed on an "AS IS" BASIS, 11 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | See the License for the specific language governing permissions and 13 | limitations under the License. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Emotion Detection 2 | 3 | Emotion Detection aims to classify a fine-grained emotion for each utterance in multiparty dialogue. 4 | Our annotation is based on the primary emotions in the Feeling Wheel (Willcox, 1982). 5 | We must admit that the inter-annotator agreement of this annotation is not the greatest; we welcome any contribution from the community to improve the annotation quality. 6 | This task is a part of the [Character Mining](../../../character-mining) project led by the [Emory NLP](http://nlp.mathcs.emory.edu) research group. 7 | 8 |

9 | 10 |

11 | 12 | 13 | ## Dataset 14 | 15 | Each utterance is annotated with one of the seven emotions, *sad*, *mad*, *scared*, *powerful*, *peaceful*, *joyful*, and *neutral*, that are the primary emotions in the Feeling Wheel. 16 | 17 | * Latest release: [v1.0](https://github.com/emorynlp/emotion-detection/archive/emotion-detection-1.0.tar.gz). 18 | * [Release notes](https://github.com/emorynlp/emotion-detection/releases). 19 | 20 | ## Statistics 21 | 22 | The following episodes are used for the training, development, and evaluation sets: 23 | 24 | * Train (TRN): [s01\_e02, s01\_e03, s01\_e04, s01\_e05, s01\_e06, s01\_e07, s01\_e08, s01\_e09, s01\_e11, s01\_e12, s01\_e13, s01\_e14, s01\_e16, s01\_e17, s01\_e18, s01\_e19, s01\_e21, s01\_e22, s01\_e23, s01\_e24, s02\_e01, s02\_e02, s02\_e03, s02\_e04, s02\_e05, s02\_e06, s02\_e07, s02\_e09, s02\_e11, s02\_e12, s02\_e13, s02\_e14, s02\_e15, s02\_e16, s02\_e17, s02\_e18, s02\_e19, s02\_e21, s02\_e22, s02\_e24, s03\_e02, s03\_e03, s03\_e04, s03\_e05, s03\_e06, s03\_e07, s03\_e10, s03\_e11, s03\_e12, s03\_e13, s03\_e14, s03\_e15, s03\_e16, s03\_e17, s03\_e18, s03\_e19, s03\_e22, s03\_e23, s03\_e24, s03\_e25, s04\_e03, s04\_e04, s04\_e05, s04\_e07, s04\_e08, s04\_e09, s04\_e11, s04\_e12, s04\_e13, s04\_e14, s04\_e15, s04\_e16, s04\_e18, s04\_e19, s04\_e22, s04\_e23, s04\_e24] 25 | * Development (DEV): [s01\_e15, s01\_e20, s02\_e10, s02\_e20, s03\_e01, s03\_e09, s03\_e21, s04\_e01, s04\_e06, s04\_e10, s04\_e21] 26 | * Evaluation (TST): [s01\_e01, s01\_e10, s02\_e08, s02\_e23, s03\_e08, s03\_e20, s04\_e02, s04\_e17, s04\_e20] 27 | 28 | | Dataset | Episodes | Scenes | Utterances | 29 | |:-------:|---------:|-------:|-----------:| 30 | | TRN | 77 | 713 | 9,934 | 31 | | DEV | 11 | 99 | 1,344 | 32 | | TST | 9 | 85 | 1,328 | 33 | | Total | 97 | 897 | 12,606 | 34 | 35 | | Dataset | Neutral | Joyful | Peaceful | Powerful | Scared | Mad | Sad | Total | 36 | |:-------:|--------:|-------:|---------:|---------:|-------:|------:|----:|-------:| 37 | | TRN | 3,034 | 2,184 | 900 | 784 | 1,285 | 1,076 | 671 | 9,934 | 38 | | DEV | 393 | 289 | 132 | 134 | 178 | 143 | 75 | 1,344 | 39 | | TST | 349 | 282 | 159 | 145 | 182 | 113 | 98 | 1,328 | 40 | | Total | 3,776 | 2,755 | 1,191 | 1,063 | 1,645 | 1,332 | 844 | 12,606 | 41 | 42 | ## Annotation 43 | 44 | Each utterance has the field `emotion`. 45 | Three utterances in the following example are annotated with the emotions of *Neutral*, *Joyful*, and *Powerful*, respectively. 46 | 47 | ```json 48 | { 49 | "utterance_id": "s01_e02_c01_u002", 50 | "speakers": ["Joey Tribbiani"], 51 | "transcript": "Yeah, right!.......Y'serious?", 52 | "tokens": [ 53 | ["Yeah", ",", "right", "!"], 54 | ["......."], 55 | ["Y'serious", "?"] 56 | ], 57 | "emotion": "Neutral" 58 | }, 59 | { 60 | "utterance_id": "s01_e02_c01_u003", 61 | "speakers": ["Phoebe Buffay"], 62 | "transcript": "Oh, yeah!", 63 | "tokens": [ 64 | ["Oh", ",", "yeah", "!"] 65 | ], 66 | "emotion": "Joyful" 67 | }, 68 | { 69 | "utterance_id": "s01_e02_c01_u004", 70 | "speakers": ["Rachel Green"], 71 | "transcript": "Everything you need to know is in that first kiss.", 72 | "tokens": [ 73 | ["Everything", "you", "need", "to", "know", "is", "in", "that", "first", "kiss", "."] 74 | ], 75 | "emotion": "Powerful" 76 | } 77 | ``` 78 | 79 | ## Citation 80 | 81 | * [Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks](https://arxiv.org/abs/1708.04299). Sayyed Zahiri and Jinho D. Choi. In The AAAI Workshop on Affective Content Analysis, AFFCON'18, 2018. 82 | 83 | 84 | ## Contact 85 | 86 | * [Jinho D. Choi](http://www.mathcs.emory.edu/~choi). 87 | --------------------------------------------------------------------------------