├── .DS_Store ├── .ipynb_checkpoints └── meng_models-checkpoint.ipynb ├── Behavior_Analysis ├── Data Exploration Plot │ ├── 01.png │ ├── 02.png │ ├── 03.png │ ├── 04.png │ ├── 05.png │ ├── 06.png │ ├── 07.png │ ├── 08.png │ ├── 09.png │ ├── 10.png │ └── 11.png └── EDA+Model.ipynb ├── README.md └── SentimentAnalysis ├── 01-data_prep_EDA.ipynb ├── 02-tokenization.ipynb ├── 03-preprocessing.ipynb ├── 04-models.ipynb ├── data └── train_df.csv └── stopwords.txt /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LakiLiu/Covid-19-Analysis/0905894869d861e6f8beab986d3b4fd6be170532/.DS_Store -------------------------------------------------------------------------------- /Behavior_Analysis/Data Exploration Plot/01.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LakiLiu/Covid-19-Analysis/0905894869d861e6f8beab986d3b4fd6be170532/Behavior_Analysis/Data Exploration Plot/01.png -------------------------------------------------------------------------------- /Behavior_Analysis/Data Exploration Plot/02.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LakiLiu/Covid-19-Analysis/0905894869d861e6f8beab986d3b4fd6be170532/Behavior_Analysis/Data Exploration Plot/02.png -------------------------------------------------------------------------------- /Behavior_Analysis/Data Exploration Plot/03.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LakiLiu/Covid-19-Analysis/0905894869d861e6f8beab986d3b4fd6be170532/Behavior_Analysis/Data Exploration Plot/03.png -------------------------------------------------------------------------------- /Behavior_Analysis/Data Exploration Plot/04.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LakiLiu/Covid-19-Analysis/0905894869d861e6f8beab986d3b4fd6be170532/Behavior_Analysis/Data Exploration Plot/04.png -------------------------------------------------------------------------------- /Behavior_Analysis/Data Exploration Plot/05.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LakiLiu/Covid-19-Analysis/0905894869d861e6f8beab986d3b4fd6be170532/Behavior_Analysis/Data Exploration Plot/05.png -------------------------------------------------------------------------------- /Behavior_Analysis/Data Exploration Plot/06.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LakiLiu/Covid-19-Analysis/0905894869d861e6f8beab986d3b4fd6be170532/Behavior_Analysis/Data Exploration Plot/06.png -------------------------------------------------------------------------------- /Behavior_Analysis/Data Exploration Plot/07.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LakiLiu/Covid-19-Analysis/0905894869d861e6f8beab986d3b4fd6be170532/Behavior_Analysis/Data Exploration Plot/07.png -------------------------------------------------------------------------------- /Behavior_Analysis/Data Exploration Plot/08.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LakiLiu/Covid-19-Analysis/0905894869d861e6f8beab986d3b4fd6be170532/Behavior_Analysis/Data Exploration Plot/08.png -------------------------------------------------------------------------------- /Behavior_Analysis/Data Exploration Plot/09.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LakiLiu/Covid-19-Analysis/0905894869d861e6f8beab986d3b4fd6be170532/Behavior_Analysis/Data Exploration Plot/09.png -------------------------------------------------------------------------------- /Behavior_Analysis/Data Exploration Plot/10.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LakiLiu/Covid-19-Analysis/0905894869d861e6f8beab986d3b4fd6be170532/Behavior_Analysis/Data Exploration Plot/10.png -------------------------------------------------------------------------------- /Behavior_Analysis/Data Exploration Plot/11.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LakiLiu/Covid-19-Analysis/0905894869d861e6f8beab986d3b4fd6be170532/Behavior_Analysis/Data Exploration Plot/11.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Covid-19-Analysis 2 | 3 | ### 1. Covid-19 Human Behaviour Analysis: A creative Reseach 4 | ##### (A) Description: 5 | - Herd Behavior is commonly used in Financial Markets to predict uncertainty. 6 | - Common Models : Econometrics Regression and Time Series 7 | - Research Value : Very Rarely applied in the Covid-19 Mobility case but Human behavior uncertainty affects the policy effectiveness 8 | 9 | #### (B) Model: 10 | - Baseline Model : Bayesian Ridge Regression 11 | 12 | --- 13 | ### 2. Covid-19 Sentiment Analysis: A Chinese NLP Study 14 | ##### (A) Description: 15 | - The dataset was based on 230 keywords related to the topic of COVID, and a total of 1 million blogs were collected from 1 January 2020 to 20 February 2020. THis includes sentiments : 1 (positive), 0 (neutral) and -1 (negative) 16 | (数据集依据与“新冠肺炎”相关的230个主题关键词进行数据采集，抓取了2020年1月1日—2020年2月20日期间共计100万条微博数据，并对其中10万条数据进行人工标注，标注分为三类，分别为：1（积极），0（中性）和-1（消极）) 17 | - 评价指标 Evaluation Matrix: Macro-F1 score 18 | 19 | #### (B) Model: 20 | - Preprocessing：tokenisation, remove duplicate, remove similar words 21 | - Machine Learning Model: Naive Bayes, Logistic regression -> result F1 score : 0.74 22 | - Deep Learning Model：RNN (RNN+LSTM)， CNN 23 | 24 | -------------------------------------------------------------------------------- /SentimentAnalysis/01-data_prep_EDA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# **Covid-19 Sentiment Analysis - Part I**\n", 8 | "This part contains data cleaning and exploratory data analysis\n", 9 | "## 1. Data Cleaning" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 1, 15 | "metadata": {}, 16 | "outputs": [ 17 | { 18 | "data": { 19 | "text/html": [ 20 | "
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IDtimeusercontentpicvideosentiment
0445607202912550001月01日 23:50存曦1988写在年末冬初孩子流感的第五天，我们仍然没有忘记热情拥抱这2020年的第一天。带着一丝迷信，早...['https://ww2.sinaimg.cn/orj360/005VnA1zly1gah...[]0
1445607416748098001月01日 23:58LunaKrys开年大模型…累到以为自己发烧了腰疼膝盖疼腿疼胳膊疼脖子疼#Luna的Krystallife#?[][]-1
2445605425326452001月01日 22:39小王爷学辩论o_O邱晨这就是我爹，爹，发烧快好，毕竟美好的假期拿来养病不太好，假期还是要好好享受快乐，爹，新...['https://ww2.sinaimg.cn/thumb150/006ymYXKgy1g...[]1
3445606150912647001月01日 23:08芩鎟新年的第一天感冒又发烧的也太衰了但是我要想着明天一定会好的?['https://ww2.sinaimg.cn/orj360/005FL9LZgy1gah...[]1
4445597932252819001月01日 17:42changlwj问：我们意念里有坏的想法了，天神就会给记下来，那如果有好的想法也会被记下来吗？答：那当然了。...[][]1
\n", 100 | "
" 101 | ], 102 | "text/plain": [ 103 | " ID time user \\\n", 104 | "0 4456072029125500 01月01日 23:50 存曦1988 \n", 105 | "1 4456074167480980 01月01日 23:58 LunaKrys \n", 106 | "2 4456054253264520 01月01日 22:39 小王爷学辩论o_O \n", 107 | "3 4456061509126470 01月01日 23:08 芩鎟 \n", 108 | "4 4455979322528190 01月01日 17:42 changlwj \n", 109 | "\n", 110 | " content \\\n", 111 | "0 写在年末冬初孩子流感的第五天，我们仍然没有忘记热情拥抱这2020年的第一天。带着一丝迷信，早... \n", 112 | "1 开年大模型…累到以为自己发烧了腰疼膝盖疼腿疼胳膊疼脖子疼#Luna的Krystallife#? \n", 113 | "2 邱晨这就是我爹，爹，发烧快好，毕竟美好的假期拿来养病不太好，假期还是要好好享受快乐，爹，新... \n", 114 | "3 新年的第一天感冒又发烧的也太衰了但是我要想着明天一定会好的? \n", 115 | "4 问：我们意念里有坏的想法了，天神就会给记下来，那如果有好的想法也会被记下来吗？答：那当然了。... \n", 116 | "\n", 117 | " pic video sentiment \n", 118 | "0 ['https://ww2.sinaimg.cn/orj360/005VnA1zly1gah... [] 0 \n", 119 | "1 [] [] -1 \n", 120 | "2 ['https://ww2.sinaimg.cn/thumb150/006ymYXKgy1g... [] 1 \n", 121 | "3 ['https://ww2.sinaimg.cn/orj360/005FL9LZgy1gah... [] 1 \n", 122 | "4 [] [] 1 " 123 | ] 124 | }, 125 | "execution_count": 1, 126 | "metadata": {}, 127 | "output_type": "execute_result" 128 | } 129 | ], 130 | "source": [ 131 | "import numpy as np\n", 132 | "import pandas as pd\n", 133 | "import matplotlib.pyplot as plt \n", 134 | "import seaborn as sns\n", 135 | "import warnings \n", 136 | "warnings.filterwarnings('ignore')\n", 137 | "\n", 138 | "path = './nCoV_100k_train.csv'\n", 139 | "with open(path,'r',encoding = 'GB18030', errors = 'ignore') as f:\n", 140 | " raw_data = pd.read_csv(f)\n", 141 | " \n", 142 | "raw_data.columns = ['ID', 'time', 'user', 'content', 'pic', 'video', 'sentiment']\n", 143 | "raw_data.head()" 144 | ] 145 | }, 146 | { 147 | "cell_type": "code", 148 | "execution_count": 2, 149 | "metadata": {}, 150 | "outputs": [ 151 | { 152 | "data": { 153 | "text/plain": [ 154 | "(100000, 7)" 155 | ] 156 | }, 157 | "execution_count": 2, 158 | "metadata": {}, 159 | "output_type": "execute_result" 160 | } 161 | ], 162 | "source": [ 163 | "raw_data.shape" 164 | ] 165 | }, 166 | { 167 | "cell_type": "markdown", 168 | "metadata": {}, 169 | "source": [ 170 | "- **target variable : sentiment**" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": 3, 176 | "metadata": {}, 177 | "outputs": [ 178 | { 179 | "data": { 180 | "text/plain": [ 181 | "(99913, 7)" 182 | ] 183 | }, 184 | "execution_count": 3, 185 | "metadata": {}, 186 | "output_type": "execute_result" 187 | } 188 | ], 189 | "source": [ 190 | "# keep y with only 1,0,-1\n", 191 | "raw_data = raw_data[(raw_data.sentiment == '0') | (raw_data.sentiment == '1') | (raw_data.sentiment == '-1')]\n", 192 | "raw_data.shape" 193 | ] 194 | }, 195 | { 196 | "cell_type": "code", 197 | "execution_count": 4, 198 | "metadata": {}, 199 | "outputs": [ 200 | { 201 | "data": { 202 | "image/png": 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" 205 | ] 206 | }, 207 | "metadata": { 208 | "needs_background": "light" 209 | }, 210 | "output_type": "display_data" 211 | } 212 | ], 213 | "source": [ 214 | "raw_data.sentiment.value_counts(normalize=True).plot(kind='bar')\n", 215 | "plt.show()" 216 | ] 217 | }, 218 | { 219 | "cell_type": "markdown", 220 | "metadata": {}, 221 | "source": [ 222 | "- **pic and video variables**" 223 | ] 224 | }, 225 | { 226 | "cell_type": "code", 227 | "execution_count": 5, 228 | "metadata": {}, 229 | "outputs": [ 230 | { 231 | "data": { 232 | "text/plain": [ 233 | "\"['https://ww2.sinaimg.cn/orj360/005VnA1zly1gahhwworn5j30m80fyq4n.jpg']\"" 234 | ] 235 | }, 236 | "execution_count": 5, 237 | "metadata": {}, 238 | "output_type": "execute_result" 239 | } 240 | ], 241 | "source": [ 242 | "raw_data.pic[0] # the url does not contain to much information, we convert photo and video to dummy variables" 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": 6, 248 | "metadata": {}, 249 | "outputs": [ 250 | { 251 | "data": { 252 | "text/html": [ 253 | "
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IDtimeusercontentpicvideosentiment
0445607202912550001月01日 23:50存曦1988写在年末冬初孩子流感的第五天，我们仍然没有忘记热情拥抱这2020年的第一天。带着一丝迷信，早...100
1445607416748098001月01日 23:58LunaKrys开年大模型…累到以为自己发烧了腰疼膝盖疼腿疼胳膊疼脖子疼#Luna的Krystallife#?00-1
2445605425326452001月01日 22:39小王爷学辩论o_O邱晨这就是我爹，爹，发烧快好，毕竟美好的假期拿来养病不太好，假期还是要好好享受快乐，爹，新...101
3445606150912647001月01日 23:08芩鎟新年的第一天感冒又发烧的也太衰了但是我要想着明天一定会好的?101
4445597932252819001月01日 17:42changlwj问：我们意念里有坏的想法了，天神就会给记下来，那如果有好的想法也会被记下来吗？答：那当然了。...001
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" 334 | ], 335 | "text/plain": [ 336 | " ID time user \\\n", 337 | "0 4456072029125500 01月01日 23:50 存曦1988 \n", 338 | "1 4456074167480980 01月01日 23:58 LunaKrys \n", 339 | "2 4456054253264520 01月01日 22:39 小王爷学辩论o_O \n", 340 | "3 4456061509126470 01月01日 23:08 芩鎟 \n", 341 | "4 4455979322528190 01月01日 17:42 changlwj \n", 342 | "\n", 343 | " content pic video sentiment \n", 344 | "0 写在年末冬初孩子流感的第五天，我们仍然没有忘记热情拥抱这2020年的第一天。带着一丝迷信，早... 1 0 0 \n", 345 | "1 开年大模型…累到以为自己发烧了腰疼膝盖疼腿疼胳膊疼脖子疼#Luna的Krystallife#? 0 0 -1 \n", 346 | "2 邱晨这就是我爹，爹，发烧快好，毕竟美好的假期拿来养病不太好，假期还是要好好享受快乐，爹，新... 1 0 1 \n", 347 | "3 新年的第一天感冒又发烧的也太衰了但是我要想着明天一定会好的? 1 0 1 \n", 348 | "4 问：我们意念里有坏的想法了，天神就会给记下来，那如果有好的想法也会被记下来吗？答：那当然了。... 0 0 1 " 349 | ] 350 | }, 351 | "execution_count": 6, 352 | "metadata": {}, 353 | "output_type": "execute_result" 354 | } 355 | ], 356 | "source": [ 357 | "def dummy(series):\n", 358 | " if series == '[]':\n", 359 | " series = 0\n", 360 | " else:\n", 361 | " series = 1\n", 362 | " return series\n", 363 | "raw_data.pic = raw_data.pic.apply(lambda x: dummy(x))\n", 364 | "raw_data.video = raw_data.video.apply(lambda x: dummy(x))\n", 365 | "raw_data.head()" 366 | ] 367 | }, 368 | { 369 | "cell_type": "markdown", 370 | "metadata": {}, 371 | "source": [ 372 | "- **drop duplicates**" 373 | ] 374 | }, 375 | { 376 | "cell_type": "code", 377 | "execution_count": 7, 378 | "metadata": {}, 379 | "outputs": [ 380 | { 381 | "data": { 382 | "text/plain": [ 383 | "4466220575191380 2\n", 384 | "4463683088077780 2\n", 385 | "4470569351337640 2\n", 386 | "4460684139742960 1\n", 387 | "4465820153259820 1\n", 388 | "Name: ID, dtype: int64" 389 | ] 390 | }, 391 | "execution_count": 7, 392 | "metadata": {}, 393 | "output_type": "execute_result" 394 | } 395 | ], 396 | "source": [ 397 | "raw_data.ID.value_counts(ascending=False).head()" 398 | ] 399 | }, 400 | { 401 | "cell_type": "code", 402 | "execution_count": 8, 403 | "metadata": {}, 404 | "outputs": [ 405 | { 406 | "data": { 407 | "text/html": [ 408 | "
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IDtimeusercontentpicvideosentiment
35202446368308807778001月22日 23:54爱你的Moment愿好人一生平安！//@努力努力再努力x:#抗击新型肺炎第一线#致敬伟大的逆行者们一定要平平安安100
62850446368308807778001月22日 23:54爱你的Moment愿好人一生平安！//@努力努力再努力x:#抗击新型肺炎第一线#致敬伟大的逆行者们一定要平平安安101
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" 459 | ], 460 | "text/plain": [ 461 | " ID time user \\\n", 462 | "35202 4463683088077780 01月22日 23:54 爱你的Moment \n", 463 | "62850 4463683088077780 01月22日 23:54 爱你的Moment \n", 464 | "\n", 465 | " content pic video sentiment \n", 466 | "35202 愿好人一生平安！//@努力努力再努力x:#抗击新型肺炎第一线#致敬伟大的逆行者们一定要平平安安 1 0 0 \n", 467 | "62850 愿好人一生平安！//@努力努力再努力x:#抗击新型肺炎第一线#致敬伟大的逆行者们一定要平平安安 1 0 1 " 468 | ] 469 | }, 470 | "execution_count": 8, 471 | "metadata": {}, 472 | "output_type": "execute_result" 473 | } 474 | ], 475 | "source": [ 476 | "raw_data[raw_data.ID == 4463683088077780]\n", 477 | "# labels are contradictory, so we drop these three observations" 478 | ] 479 | }, 480 | { 481 | "cell_type": "code", 482 | "execution_count": 9, 483 | "metadata": {}, 484 | "outputs": [ 485 | { 486 | "data": { 487 | "text/plain": [ 488 | "(99907, 7)" 489 | ] 490 | }, 491 | "execution_count": 9, 492 | "metadata": {}, 493 | "output_type": "execute_result" 494 | } 495 | ], 496 | "source": [ 497 | "raw_data = raw_data[(raw_data.ID !=4470569351337640) & (raw_data.ID !=4463683088077780) & (raw_data.ID !=4466220575191380)]\n", 498 | "raw_data.shape" 499 | ] 500 | }, 501 | { 502 | "cell_type": "markdown", 503 | "metadata": {}, 504 | "source": [ 505 | "- **time**" 506 | ] 507 | }, 508 | { 509 | "cell_type": "code", 510 | "execution_count": 10, 511 | "metadata": {}, 512 | "outputs": [ 513 | { 514 | "data": { 515 | "text/html": [ 516 | "
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IDtimeusercontentpicvideosentiment
044560720291255002020-01-01 23:50:00存曦1988写在年末冬初孩子流感的第五天，我们仍然没有忘记热情拥抱这2020年的第一天。带着一丝迷信，早...100
144560741674809802020-01-01 23:58:00LunaKrys开年大模型…累到以为自己发烧了腰疼膝盖疼腿疼胳膊疼脖子疼#Luna的Krystallife#?00-1
244560542532645202020-01-01 22:39:00小王爷学辩论o_O邱晨这就是我爹，爹，发烧快好，毕竟美好的假期拿来养病不太好，假期还是要好好享受快乐，爹，新...101
344560615091264702020-01-01 23:08:00芩鎟新年的第一天感冒又发烧的也太衰了但是我要想着明天一定会好的?101
444559793225281902020-01-01 17:42:00changlwj问：我们意念里有坏的想法了，天神就会给记下来，那如果有好的想法也会被记下来吗？答：那当然了。...001
\n", 596 | "
" 597 | ], 598 | "text/plain": [ 599 | " ID time user \\\n", 600 | "0 4456072029125500 2020-01-01 23:50:00 存曦1988 \n", 601 | "1 4456074167480980 2020-01-01 23:58:00 LunaKrys \n", 602 | "2 4456054253264520 2020-01-01 22:39:00 小王爷学辩论o_O \n", 603 | "3 4456061509126470 2020-01-01 23:08:00 芩鎟 \n", 604 | "4 4455979322528190 2020-01-01 17:42:00 changlwj \n", 605 | "\n", 606 | " content pic video sentiment \n", 607 | "0 写在年末冬初孩子流感的第五天，我们仍然没有忘记热情拥抱这2020年的第一天。带着一丝迷信，早... 1 0 0 \n", 608 | "1 开年大模型…累到以为自己发烧了腰疼膝盖疼腿疼胳膊疼脖子疼#Luna的Krystallife#? 0 0 -1 \n", 609 | "2 邱晨这就是我爹，爹，发烧快好，毕竟美好的假期拿来养病不太好，假期还是要好好享受快乐，爹，新... 1 0 1 \n", 610 | "3 新年的第一天感冒又发烧的也太衰了但是我要想着明天一定会好的? 1 0 1 \n", 611 | "4 问：我们意念里有坏的想法了，天神就会给记下来，那如果有好的想法也会被记下来吗？答：那当然了。... 0 0 1 " 612 | ] 613 | }, 614 | "execution_count": 10, 615 | "metadata": {}, 616 | "output_type": "execute_result" 617 | } 618 | ], 619 | "source": [ 620 | "raw_data.time = pd.to_datetime('2020年'+raw_data.time,errors='coerce', format='%Y年%m月%d日 %H:%M')\n", 621 | "raw_data.head()" 622 | ] 623 | }, 624 | { 625 | "cell_type": "code", 626 | "execution_count": 11, 627 | "metadata": {}, 628 | "outputs": [], 629 | "source": [ 630 | "# save to file\n", 631 | "raw_data.drop(['user'], axis=1, inplace=True)\n", 632 | "raw_data.to_csv('train_df.csv',index_label=False)" 633 | ] 634 | }, 635 | { 636 | "cell_type": "markdown", 637 | "metadata": {}, 638 | "source": [ 639 | "## 2. EDA " 640 | ] 641 | } 642 | ], 643 | "metadata": { 644 | "kernelspec": { 645 | "display_name": "Python 3", 646 | "language": "python", 647 | "name": "python3" 648 | }, 649 | "language_info": { 650 | "codemirror_mode": { 651 | "name": "ipython", 652 | "version": 3 653 | }, 654 | "file_extension": ".py", 655 | "mimetype": "text/x-python", 656 | "name": "python", 657 | "nbconvert_exporter": "python", 658 | "pygments_lexer": "ipython3", 659 | "version": "3.7.6" 660 | } 661 | }, 662 | "nbformat": 4, 663 | "nbformat_minor": 4 664 | } 665 | -------------------------------------------------------------------------------- /SentimentAnalysis/02-tokenization.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# **Covid-19 Sentiment Analysis - Part II**\n", 8 | "This part contains the word tokenizaiton and cleaning process" 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "metadata": {}, 15 | "outputs": [], 16 | "source": [ 17 | "import pandas as pd\n", 18 | "import thulac\n", 19 | "import seaborn as sns\n", 20 | "import matplotlib.pyplot as plt\n", 21 | "\n", 22 | "myfile = './train_df.csv'\n", 23 | "with open(myfile,'r', errors = 'ignore') as f:\n", 24 | " raw_data = pd.read_csv(f,)\n", 25 | " \n", 26 | "# raw_data = raw_data.rename(columns={\"微博id\": \"ID\", \"微博发布时间\": \"time\", '发布人账号':'user','微博中文内容':'content',\n", 27 | "# '微博图片':'pic', '微博视频':'video','情感倾向':'sentiment'})" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 2, 33 | "metadata": {}, 34 | "outputs": [], 35 | "source": [ 36 | "df = raw_data.copy()" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 3, 42 | "metadata": {}, 43 | "outputs": [ 44 | { 45 | "data": { 46 | "text/plain": [ 47 | "(99907, 6)" 48 | ] 49 | }, 50 | "execution_count": 3, 51 | "metadata": {}, 52 | "output_type": "execute_result" 53 | } 54 | ], 55 | "source": [ 56 | "df.shape" 57 | ] 58 | }, 59 | { 60 | "cell_type": "markdown", 61 | "metadata": {}, 62 | "source": [ 63 | "## 1. Constraint content length \n", 64 | "According to density plot, we only keep contents <= 150 words" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 4, 70 | "metadata": {}, 71 | "outputs": [ 72 | { 73 | "data": { 74 | "image/png": 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" 77 | ] 78 | }, 79 | "metadata": { 80 | "needs_background": "light" 81 | }, 82 | "output_type": "display_data" 83 | } 84 | ], 85 | "source": [ 86 | "df['weibo_len'] = df['content'].astype(str).apply(len)\n", 87 | "sns.kdeplot(df['weibo_len'])\n", 88 | "plt.title('weibo_len')\n", 89 | "plt.show()" 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": 5, 95 | "metadata": {}, 96 | "outputs": [ 97 | { 98 | "data": { 99 | "text/plain": [ 100 | "(93361, 7)" 101 | ] 102 | }, 103 | "execution_count": 5, 104 | "metadata": {}, 105 | "output_type": "execute_result" 106 | } 107 | ], 108 | "source": [ 109 | "df = df[df.weibo_len<=150]\n", 110 | "df.shape" 111 | ] 112 | }, 113 | { 114 | "cell_type": "markdown", 115 | "metadata": {}, 116 | "source": [ 117 | "## 3.Remove symbols and non-Chinese" 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": 6, 123 | "metadata": {}, 124 | "outputs": [ 125 | { 126 | "data": { 127 | "text/plain": [ 128 | "'写在年末冬初孩子流感的第五天，我们仍然没有忘记热情拥抱这2020年的第一天。带着一丝迷信，早晨给孩子穿上红色的羽绒服羽绒裤，祈祷新的一年，孩子们身体康健。仍然会有一丝焦虑，焦虑我的孩子为什么会过早的懂事，从两岁多开始关注我的情绪，会深沉地说：妈妈，你终于笑了！这句话像刀子一样扎入我?展开全文c'" 129 | ] 130 | }, 131 | "execution_count": 6, 132 | "metadata": {}, 133 | "output_type": "execute_result" 134 | } 135 | ], 136 | "source": [ 137 | "df.content[0]" 138 | ] 139 | }, 140 | { 141 | "cell_type": "code", 142 | "execution_count": 7, 143 | "metadata": {}, 144 | "outputs": [], 145 | "source": [ 146 | "import regex as re\n", 147 | "# remove symbol, number and letter\n", 148 | "symbols = \"[a-zA-Z0-9\\s+\\.\\!\\/_,$%^*()??;；：【】+\\\"\\'\\[\\]\\\\]+|[+——！，;:。？《》、~@#￥%……&*（）“”.=-]+\"\n", 149 | "df['content'] = df['content'].astype(str).apply(lambda x : re.sub(symbols, '', x))\n", 150 | "\n", 151 | "#pre-cleaning, deleting or filtering unnecessary words like 'show more'\n", 152 | "df['content'] = df['content'].astype(str).apply(lambda x : x.replace(\"展开全文\", \"\"))\n", 153 | "\n", 154 | "#remove non-chinese\n", 155 | "pattern = re.compile(r'[^\\u4e00-\\u9fa5]')\n", 156 | "df['content'] = df['content'].astype(str).apply(lambda x : re.sub(pattern,'',x))" 157 | ] 158 | }, 159 | { 160 | "cell_type": "code", 161 | "execution_count": 8, 162 | "metadata": {}, 163 | "outputs": [ 164 | { 165 | "data": { 166 | "text/html": [ 167 | "
\n", 168 | "\n", 181 | "\n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | "
IDtimecontentpicvideosentimentweibo_len
044560720291255002020-01-01 23:50:00写在年末冬初孩子流感的第五天我们仍然没有忘记热情拥抱这年的第一天带着一丝迷信早晨给孩子穿上红...100147
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244560542532645202020-01-01 22:39:00邱晨这就是我爹爹发烧快好毕竟美好的假期拿来养病不太好假期还是要好好享受快乐爹新年快乐发烧好了...10199
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444559793225281902020-01-01 17:42:00问我们意念里有坏的想法了天神就会给记下来那如果有好的想法也会被记下来吗答那当然了有坏的想法天...001145
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" 248 | ], 249 | "text/plain": [ 250 | " ID time \\\n", 251 | "0 4456072029125500 2020-01-01 23:50:00 \n", 252 | "1 4456074167480980 2020-01-01 23:58:00 \n", 253 | "2 4456054253264520 2020-01-01 22:39:00 \n", 254 | "3 4456061509126470 2020-01-01 23:08:00 \n", 255 | "4 4455979322528190 2020-01-01 17:42:00 \n", 256 | "\n", 257 | " content pic video sentiment \\\n", 258 | "0 写在年末冬初孩子流感的第五天我们仍然没有忘记热情拥抱这年的第一天带着一丝迷信早晨给孩子穿上红... 1 0 0 \n", 259 | "1 开年大模型累到以为自己发烧了腰疼膝盖疼腿疼胳膊疼脖子疼的 0 0 -1 \n", 260 | "2 邱晨这就是我爹爹发烧快好毕竟美好的假期拿来养病不太好假期还是要好好享受快乐爹新年快乐发烧好了... 1 0 1 \n", 261 | "3 新年的第一天感冒又发烧的也太衰了但是我要想着明天一定会好的 1 0 1 \n", 262 | "4 问我们意念里有坏的想法了天神就会给记下来那如果有好的想法也会被记下来吗答那当然了有坏的想法天... 0 0 1 \n", 263 | "\n", 264 | " weibo_len \n", 265 | "0 147 \n", 266 | "1 47 \n", 267 | "2 99 \n", 268 | "3 30 \n", 269 | "4 145 " 270 | ] 271 | }, 272 | "execution_count": 8, 273 | "metadata": {}, 274 | "output_type": "execute_result" 275 | } 276 | ], 277 | "source": [ 278 | "df.head()" 279 | ] 280 | }, 281 | { 282 | "cell_type": "code", 283 | "execution_count": 9, 284 | "metadata": {}, 285 | "outputs": [ 286 | { 287 | "data": { 288 | "text/plain": [ 289 | "'写在年末冬初孩子流感的第五天我们仍然没有忘记热情拥抱这年的第一天带着一丝迷信早晨给孩子穿上红色的羽绒服羽绒裤祈祷新的一年孩子们身体康健仍然会有一丝焦虑焦虑我的孩子为什么会过早的懂事从两岁多开始关注我的情绪会深沉地说妈妈你终于笑了这句话像刀子一样扎入我'" 290 | ] 291 | }, 292 | "execution_count": 9, 293 | "metadata": {}, 294 | "output_type": "execute_result" 295 | } 296 | ], 297 | "source": [ 298 | "df.content[0]" 299 | ] 300 | }, 301 | { 302 | "cell_type": "markdown", 303 | "metadata": {}, 304 | "source": [ 305 | "## 2. Word Tokenization" 306 | ] 307 | }, 308 | { 309 | "cell_type": "code", 310 | "execution_count": 10, 311 | "metadata": {}, 312 | "outputs": [ 313 | { 314 | "name": "stdout", 315 | "output_type": "stream", 316 | "text": [ 317 | "Model loaded succeed\n" 318 | ] 319 | }, 320 | { 321 | "data": { 322 | "text/html": [ 323 | "
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IDtimecontentpicvideosentimentweibo_len
044560720291255002020-01-01 23:50:00写 在 年末 冬初 孩子 流感 的 第五 天 我们 仍然 没有 忘记 热情 拥抱 这 年 的...100147
144560741674809802020-01-01 23:58:00开 年 大模型 累 到 以为 自己 发烧 了 腰 疼 膝盖 疼 腿 疼 胳膊 疼 脖子 疼 的00-147
244560542532645202020-01-01 22:39:00邱晨 这 就 是 我 爹 爹 发烧 快 好 毕竟 美好 的 假期 拿 来 养 病 不 太 好...10199
344560615091264702020-01-01 23:08:00新年 的 第一 天 感冒 又 发烧 的 也 太 衰 了 但是 我 要 想 着 明天 一定 会...10130
444559793225281902020-01-01 17:42:00问 我们 意念 里 有 坏 的 想法 了 天神 就 会 给 记 下 来 那 如果 有 好 的...001145
544559607035742702020-01-01 16:28:00发高烧 反反复复 眼睛 都 快 睁 不 开 了 今天 室友 带 我 去 看 还 在 发烧 中...10-1139
644560441413113702020-01-01 21:59:00明天 考试 今天 发烧 跨年 给 我 跨 坏 了 兰州 兰州 交通 大学00-128
744560729305973802020-01-01 23:53:00元旦 快乐 枇杷 手法 小结 每个 娃 都 是 有 故事 的 娃 每个 大人 也 是 有 故...100143
844560595467663202020-01-01 23:00:00我 真 的 服 了 昨天 去 和 她 说 自己 不 舒服 描述 了 症状 她 说 啊 你 这...00-1144
944560643617302002020-01-01 23:19:00新年 第一 天 为 自己 鼓掌 发烧 了 也 要 来 看 线 下 演出 因为 热爱 所以 才...101127
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" 454 | ], 455 | "text/plain": [ 456 | " ID time \\\n", 457 | "0 4456072029125500 2020-01-01 23:50:00 \n", 458 | "1 4456074167480980 2020-01-01 23:58:00 \n", 459 | "2 4456054253264520 2020-01-01 22:39:00 \n", 460 | "3 4456061509126470 2020-01-01 23:08:00 \n", 461 | "4 4455979322528190 2020-01-01 17:42:00 \n", 462 | "5 4455960703574270 2020-01-01 16:28:00 \n", 463 | "6 4456044141311370 2020-01-01 21:59:00 \n", 464 | "7 4456072930597380 2020-01-01 23:53:00 \n", 465 | "8 4456059546766320 2020-01-01 23:00:00 \n", 466 | "9 4456064361730200 2020-01-01 23:19:00 \n", 467 | "\n", 468 | " content pic video sentiment \\\n", 469 | "0 写 在 年末 冬初 孩子 流感 的 第五 天 我们 仍然 没有 忘记 热情 拥抱 这 年 的... 1 0 0 \n", 470 | "1 开 年 大模型 累 到 以为 自己 发烧 了 腰 疼 膝盖 疼 腿 疼 胳膊 疼 脖子 疼 的 0 0 -1 \n", 471 | "2 邱晨 这 就 是 我 爹 爹 发烧 快 好 毕竟 美好 的 假期 拿 来 养 病 不 太 好... 1 0 1 \n", 472 | "3 新年 的 第一 天 感冒 又 发烧 的 也 太 衰 了 但是 我 要 想 着 明天 一定 会... 1 0 1 \n", 473 | "4 问 我们 意念 里 有 坏 的 想法 了 天神 就 会 给 记 下 来 那 如果 有 好 的... 0 0 1 \n", 474 | "5 发高烧 反反复复 眼睛 都 快 睁 不 开 了 今天 室友 带 我 去 看 还 在 发烧 中... 1 0 -1 \n", 475 | "6 明天 考试 今天 发烧 跨年 给 我 跨 坏 了 兰州 兰州 交通 大学 0 0 -1 \n", 476 | "7 元旦 快乐 枇杷 手法 小结 每个 娃 都 是 有 故事 的 娃 每个 大人 也 是 有 故... 1 0 0 \n", 477 | "8 我 真 的 服 了 昨天 去 和 她 说 自己 不 舒服 描述 了 症状 她 说 啊 你 这... 0 0 -1 \n", 478 | "9 新年 第一 天 为 自己 鼓掌 发烧 了 也 要 来 看 线 下 演出 因为 热爱 所以 才... 1 0 1 \n", 479 | "\n", 480 | " weibo_len \n", 481 | "0 147 \n", 482 | "1 47 \n", 483 | "2 99 \n", 484 | "3 30 \n", 485 | "4 145 \n", 486 | "5 139 \n", 487 | "6 28 \n", 488 | "7 143 \n", 489 | "8 144 \n", 490 | "9 127 " 491 | ] 492 | }, 493 | "execution_count": 10, 494 | "metadata": {}, 495 | "output_type": "execute_result" 496 | } 497 | ], 498 | "source": [ 499 | "# tokenize by http://thulac.thunlp.org/\n", 500 | "\n", 501 | "thul = thulac.thulac(seg_only=True) # by default\n", 502 | "\n", 503 | "df['content'] = df['content'].astype(str).apply(lambda x : thul.cut(x,text = True))\n", 504 | "#text = thu1.cut(df0.content[0], text=True) #进行一句话分词\n", 505 | "#print(text)\n", 506 | "df.head(10)" 507 | ] 508 | }, 509 | { 510 | "cell_type": "markdown", 511 | "metadata": {}, 512 | "source": [ 513 | "## 4. Remove meaningfulless words\n", 514 | "Import word list and filter word lists during the count vectorizer part\n", 515 | "e.g. stopwords, adverbs..." 516 | ] 517 | }, 518 | { 519 | "cell_type": "code", 520 | "execution_count": 11, 521 | "metadata": {}, 522 | "outputs": [ 523 | { 524 | "data": { 525 | "text/plain": [ 526 | "(1893, 1)" 527 | ] 528 | }, 529 | "execution_count": 11, 530 | "metadata": {}, 531 | "output_type": "execute_result" 532 | } 533 | ], 534 | "source": [ 535 | "import csv\n", 536 | "myfile = './mystopwords.csv'\n", 537 | "\n", 538 | "stopwords = pd.read_csv(myfile, delimiter='\\n', header=None, encoding='utf-8', quoting=csv.QUOTE_NONE)\n", 539 | "stopwords.shape" 540 | ] 541 | }, 542 | { 543 | "cell_type": "code", 544 | "execution_count": 12, 545 | "metadata": { 546 | "collapsed": true, 547 | "jupyter": { 548 | "outputs_hidden": true 549 | } 550 | }, 551 | "outputs": [ 552 | { 553 | "name": "stderr", 554 | "output_type": "stream", 555 | "text": [ 556 | "/anaconda3/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:300: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['lex', '①①', '①②', '①③', '①④', '①⑤', '①⑥', '①⑦', '①⑧', '①⑨', '①ａ', '①ｂ', '①ｃ', '①ｄ', '①ｅ', '①ｆ', '①ｇ', '①ｈ', '①ｉ', '①ｏ', '②①', '②②', '②③', '②④', '②⑤', '②⑥', '②⑦', '②⑧', '②⑩', '②ａ', '②ｂ', '②ｄ', '②ｅ', '②ｆ', '②ｇ', '②ｈ', '②ｉ', '②ｊ', '③①', '③⑩', '③ａ', '③ｂ', '③ｃ', '③ｄ', '③ｅ', '③ｆ', '③ｇ', '③ｈ', '④ａ', '④ｂ', '④ｃ', '④ｄ', '④ｅ', '⑤ａ', '⑤ｂ', '⑤ｄ', '⑤ｅ', '⑤ｆ', '１２', 'ｌｉ', 'ｚｘｆｉｔｌ'] not in stop_words.\n", 557 | " 'stop_words.' % sorted(inconsistent))\n" 558 | ] 559 | }, 560 | { 561 | "data": { 562 | "text/plain": [ 563 | "{'酒精': 161,\n", 564 | " '网页链': 150,\n", 565 | " '社区': 141,\n", 566 | " '口罩': 53,\n", 567 | " '医用': 41,\n", 568 | " '工作': 69,\n", 569 | " '疫情': 129,\n", 570 | " '消毒': 122,\n", 571 | " '小时': 68,\n", 572 | " '病例': 133,\n", 573 | " '确诊': 139,\n", 574 | " '第一': 145,\n", 575 | " '患者': 79,\n", 576 | " '政府': 99,\n", 577 | " '重症': 162,\n", 578 | " '肺炎': 152,\n", 579 | " '疾病': 131,\n", 580 | " '武汉': 116,\n", 581 | " '市场': 70,\n", 582 | " '感冒': 81,\n", 583 | " '病毒': 134,\n", 584 | " '研究': 138,\n", 585 | " '上海': 1,\n", 586 | " '预防': 171,\n", 587 | " '上班': 2,\n", 588 | " '老师': 151,\n", 589 | " '视频': 154,\n", 590 | " '微博': 76,\n", 591 | " '隔离': 169,\n", 592 | " '中国': 6,\n", 593 | " '野味': 163,\n", 594 | " '春节': 109,\n", 595 | " '希望': 71,\n", 596 | " '晚上': 110,\n", 597 | " '出院': 30,\n", 598 | " '影响': 75,\n", 599 | " '感染': 82,\n", 600 | " '加油': 34,\n", 601 | " '致敬': 153,\n", 602 | " '前线': 32,\n", 603 | " '医护': 39,\n", 604 | " '人员': 11,\n", 605 | " '感谢': 84,\n", 606 | " '抗击': 89,\n", 607 | " '新型': 103,\n", 608 | " '第一线': 146,\n", 609 | " '湖北': 123,\n", 610 | " '社会': 140,\n", 611 | " '冠状': 28,\n", 612 | " '动物': 35,\n", 613 | " '发生': 52,\n", 614 | " '出门': 29,\n", 615 | " '今日': 12,\n", 616 | " '发现': 51,\n", 617 | " '新增': 104,\n", 618 | " '消息': 121,\n", 619 | " '评论': 156,\n", 620 | " '发热': 50,\n", 621 | " '困难': 57,\n", 622 | " '控制': 95,\n", 623 | " '建议': 73,\n", 624 | " '医院': 44,\n", 625 | " '支持': 97,\n", 626 | " '基层': 60,\n", 627 | " '防疫': 167,\n", 628 | " '防控': 166,\n", 629 | " '公益': 26,\n", 630 | " '发布': 48,\n", 631 | " '增强': 61,\n", 632 | " '抵抗力': 92,\n", 633 | " '转发': 158,\n", 634 | " '特别': 125,\n", 635 | " '领导': 172,\n", 636 | " '期间': 114,\n", 637 | " '咳嗽': 55,\n", 638 | " '生活': 127,\n", 639 | " '全国': 22,\n", 640 | " '新冠肺炎': 102,\n", 641 | " '月日': 112,\n", 642 | " '医务': 38,\n", 643 | " '新闻': 105,\n", 644 | " '喜欢': 56,\n", 645 | " '防护': 165,\n", 646 | " '公司': 25,\n", 647 | " '身体': 157,\n", 648 | " '医生': 40,\n", 649 | " '措施': 96,\n", 650 | " '平安': 72,\n", 651 | " '健康': 21,\n", 652 | " '辛苦': 159,\n", 653 | " '钟南山': 164,\n", 654 | " '原因': 47,\n", 655 | " '地方': 58,\n", 656 | " '朋友': 113,\n", 657 | " '情况': 80,\n", 658 | " '抗疫': 90,\n", 659 | " '一线': 0,\n", 660 | " '奋战': 64,\n", 661 | " '公共': 24,\n", 662 | " '卫生': 46,\n", 663 | " '传染病': 17,\n", 664 | " '传播': 15,\n", 665 | " '传人': 14,\n", 666 | " '手机': 87,\n", 667 | " '院士': 168,\n", 668 | " '捐赠': 93,\n", 669 | " '卫健委': 45,\n", 670 | " '发烧': 49,\n", 671 | " '症状': 135,\n", 672 | " '检测': 115,\n", 673 | " '治疗': 119,\n", 674 | " '企业': 13,\n", 675 | " '复工': 62,\n", 676 | " '护士': 91,\n", 677 | " '治愈': 118,\n", 678 | " '小区': 67,\n", 679 | " '物资': 124,\n", 680 | " '志愿者': 77,\n", 681 | " '祈福': 142,\n", 682 | " '直播': 136,\n", 683 | " '方案': 106,\n", 684 | " '分享': 31,\n", 685 | " '医疗': 42,\n", 686 | " '战疫': 85,\n", 687 | " '关注': 27,\n", 688 | " '城市': 59,\n", 689 | " '感觉': 83,\n", 690 | " '武汉市': 117,\n", 691 | " '紧急': 147,\n", 692 | " '世界': 4,\n", 693 | " '接触': 94,\n", 694 | " '疑似': 128,\n", 695 | " '孩子': 66,\n", 696 | " '快乐': 78,\n", 697 | " '组织': 148,\n", 698 | " '时间': 107,\n", 699 | " '活动': 120,\n", 700 | " '为什': 9,\n", 701 | " '通知': 160,\n", 702 | " '好好': 65,\n", 703 | " '戴口罩': 86,\n", 704 | " '传染': 16,\n", 705 | " '记者': 155,\n", 706 | " '临床': 8,\n", 707 | " '保护': 19,\n", 708 | " '体温': 18,\n", 709 | " '病人': 132,\n", 710 | " '中心': 7,\n", 711 | " '新冠': 101,\n", 712 | " '中医药': 5,\n", 713 | " '医疗队': 43,\n", 714 | " '大学': 63,\n", 715 | " '相关': 137,\n", 716 | " '力量': 33,\n", 717 | " '呼吸': 54,\n", 718 | " '明天': 108,\n", 719 | " '全球': 23,\n", 720 | " '科学': 143,\n", 721 | " '疫苗': 130,\n", 722 | " '空气': 144,\n", 723 | " '开学': 74,\n", 724 | " '页链': 170,\n", 725 | " '努力': 36,\n", 726 | " '结束': 149,\n", 727 | " '下午': 3,\n", 728 | " '北京': 37,\n", 729 | " '信息': 20,\n", 730 | " '最新': 111,\n", 731 | " '打卡': 88,\n", 732 | " '救治': 100,\n", 733 | " '事件': 10,\n", 734 | " '支援': 98,\n", 735 | " '生命': 126}" 736 | ] 737 | }, 738 | "execution_count": 12, 739 | "metadata": {}, 740 | "output_type": "execute_result" 741 | } 742 | ], 743 | "source": [ 744 | "from sklearn.model_selection import train_test_split\n", 745 | "from sklearn.feature_extraction.text import TfidfVectorizer\n", 746 | "\n", 747 | "\n", 748 | "X_train_pre, X_test_pre, y_train, y_test = train_test_split(df['content'].astype(str), df['sentiment'].tolist(), test_size=0.2, random_state=0)\n", 749 | "\n", 750 | "tfidf = TfidfVectorizer(ngram_range = (1,1), stop_words = list(stopwords.values.reshape(1893,)), analyzer = 'word', min_df=0.01)\n", 751 | "tfidffit = tfidf.fit(X_train_pre)\n", 752 | "X_train = tfidffit.transform(X_train_pre)\n", 753 | "tfidf.vocabulary_" 754 | ] 755 | }, 756 | { 757 | "cell_type": "code", 758 | "execution_count": 13, 759 | "metadata": {}, 760 | "outputs": [ 761 | { 762 | "data": { 763 | "text/plain": [ 764 | "<74688x173 sparse matrix of type ''\n", 765 | "\twith 321189 stored elements in Compressed Sparse Row format>" 766 | ] 767 | }, 768 | "execution_count": 13, 769 | "metadata": {}, 770 | "output_type": "execute_result" 771 | } 772 | ], 773 | "source": [ 774 | "X_train" 775 | ] 776 | } 777 | ], 778 | "metadata": { 779 | "kernelspec": { 780 | "display_name": "Python 3", 781 | "language": "python", 782 | "name": "python3" 783 | }, 784 | "language_info": { 785 | "codemirror_mode": { 786 | "name": "ipython", 787 | "version": 3 788 | }, 789 | "file_extension": ".py", 790 | "mimetype": "text/x-python", 791 | "name": "python", 792 | "nbconvert_exporter": "python", 793 | "pygments_lexer": "ipython3", 794 | "version": "3.7.6" 795 | } 796 | }, 797 | "nbformat": 4, 798 | "nbformat_minor": 4 799 | } 800 | -------------------------------------------------------------------------------- /SentimentAnalysis/03-preprocessing.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 thulac\n", 11 | "import seaborn as sns\n", 12 | "import matplotlib.pyplot as plt\n", 13 | "import regex as re\n", 14 | "import csv" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 2, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "myfile = '/Users/zhangmeng/Desktop/study/ESCP_SEP/NLP/sentiment/train_ dataset/labled.csv'\n", 24 | "with open(myfile,'r',encoding = 'GB18030', errors = 'ignore') as f:\n", 25 | " raw_data = pd.read_csv(f)" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 3, 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "raw_data = raw_data.rename(columns={\"微博id\": \"ID\", \"微博发布时间\": \"time\", '发布人账号':'user','微博中文内容':'content',\n", 35 | " '微博图片':'pic', '微博视频':'video','情感倾向':'sentiment'})" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 30, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "df0 = raw_data\n", 45 | "df0 = df0.drop(columns=['time','user','pic', 'video'])" 46 | ] 47 | }, 48 | { 49 | "cell_type": "markdown", 50 | "metadata": {}, 51 | "source": [ 52 | "# Understand data, check target value distribution and the distribution of Weibo length" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": 31, 58 | "metadata": {}, 59 | "outputs": [ 60 | { 61 | "data": { 62 | "text/plain": [ 63 | "Text(0.5,1,'sentiment(target) distribution')" 64 | ] 65 | }, 66 | "execution_count": 31, 67 | "metadata": {}, 68 | "output_type": "execute_result" 69 | }, 70 | { 71 | "data": { 72 | "image/png": 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\n", 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" 75 | ] 76 | }, 77 | "metadata": { 78 | "needs_background": "light" 79 | }, 80 | "output_type": "display_data" 81 | } 82 | ], 83 | "source": [ 84 | "df0.sentiment.value_counts().plot.bar()\n", 85 | "plt.title('sentiment(target) distribution')" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": 34, 91 | "metadata": {}, 92 | "outputs": [], 93 | "source": [ 94 | "df0['sentiment'] = df0['sentiment'].astype(str)\n", 95 | "valid_value = [\"0\",\"1\",\"-1\"]\n", 96 | "is_valid = df0['sentiment'].isin(valid_value)\n", 97 | "df0 = df0[is_valid]" 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": 36, 103 | "metadata": {}, 104 | "outputs": [ 105 | { 106 | "data": { 107 | "text/plain": [ 108 | "Text(0.5,1,'sentiment(target) distribution')" 109 | ] 110 | }, 111 | "execution_count": 36, 112 | "metadata": {}, 113 | "output_type": "execute_result" 114 | }, 115 | { 116 | "data": { 117 | "image/png": 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\n", 118 | "text/plain": [ 119 | "
" 120 | ] 121 | }, 122 | "metadata": { 123 | "needs_background": "light" 124 | }, 125 | "output_type": "display_data" 126 | } 127 | ], 128 | "source": [ 129 | "df0.sentiment.value_counts().plot.bar()\n", 130 | "plt.title('sentiment(target) distribution')" 131 | ] 132 | }, 133 | { 134 | "cell_type": "code", 135 | "execution_count": 37, 136 | "metadata": {}, 137 | "outputs": [ 138 | { 139 | "data": { 140 | "text/plain": [ 141 | "Text(0.5,1,'sentiment(target) distribution')" 142 | ] 143 | }, 144 | "execution_count": 37, 145 | "metadata": {}, 146 | "output_type": "execute_result" 147 | }, 148 | { 149 | "data": { 150 | "image/png": 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\n", 151 | "text/plain": [ 152 | "
" 153 | ] 154 | }, 155 | "metadata": { 156 | "needs_background": "light" 157 | }, 158 | "output_type": "display_data" 159 | } 160 | ], 161 | "source": [ 162 | "df0.sentiment.value_counts().plot.bar()\n", 163 | "plt.title('sentiment(target) distribution')" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": 38, 169 | "metadata": {}, 170 | "outputs": [ 171 | { 172 | "name": "stderr", 173 | "output_type": "stream", 174 | "text": [ 175 | "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", 176 | "A value is trying to be set on a copy of a slice from a DataFrame.\n", 177 | "Try using .loc[row_indexer,col_indexer] = value instead\n", 178 | "\n", 179 | "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", 180 | " \"\"\"Entry point for launching an IPython kernel.\n" 181 | ] 182 | }, 183 | { 184 | "data": { 185 | "text/plain": [ 186 | "Text(0.5,1,'weibo length')" 187 | ] 188 | }, 189 | "execution_count": 38, 190 | "metadata": {}, 191 | "output_type": "execute_result" 192 | }, 193 | { 194 | "data": { 195 | "image/png": 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\n", 196 | "text/plain": [ 197 | "
" 198 | ] 199 | }, 200 | "metadata": { 201 | "needs_background": "light" 202 | }, 203 | "output_type": "display_data" 204 | } 205 | ], 206 | "source": [ 207 | "df0['weibo_len'] = df0['content'].astype(str).apply(len)\n", 208 | "sns.kdeplot(df0['weibo_len'])\n", 209 | "plt.title('weibo length')" 210 | ] 211 | }, 212 | { 213 | "cell_type": "code", 214 | "execution_count": 39, 215 | "metadata": {}, 216 | "outputs": [ 217 | { 218 | "data": { 219 | "text/plain": [ 220 | "Text(0.5,1,'weibo length new')" 221 | ] 222 | }, 223 | "execution_count": 39, 224 | "metadata": {}, 225 | "output_type": "execute_result" 226 | }, 227 | { 228 | "data": { 229 | "image/png": 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\n", 230 | "text/plain": [ 231 | "
" 232 | ] 233 | }, 234 | "metadata": { 235 | "needs_background": "light" 236 | }, 237 | "output_type": "display_data" 238 | } 239 | ], 240 | "source": [ 241 | "df1 = df0[df0.content.astype(str).apply(len)<=170]\n", 242 | "sns.kdeplot(df1['weibo_len'])\n", 243 | "plt.title('weibo length new')" 244 | ] 245 | }, 246 | { 247 | "cell_type": "markdown", 248 | "metadata": {}, 249 | "source": [ 250 | "# pre-cleaning, deleting or filtering unnecessary words like 'show more'" 251 | ] 252 | }, 253 | { 254 | "cell_type": "code", 255 | "execution_count": 40, 256 | "metadata": {}, 257 | "outputs": [ 258 | { 259 | "name": "stderr", 260 | "output_type": "stream", 261 | "text": [ 262 | "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", 263 | "A value is trying to be set on a copy of a slice from a DataFrame.\n", 264 | "Try using .loc[row_indexer,col_indexer] = value instead\n", 265 | "\n", 266 | "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", 267 | " \n", 268 | "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n", 269 | "A value is trying to be set on a copy of a slice from a DataFrame.\n", 270 | "Try using .loc[row_indexer,col_indexer] = value instead\n", 271 | "\n", 272 | "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", 273 | " \"\"\"\n" 274 | ] 275 | } 276 | ], 277 | "source": [ 278 | "#remove \"show more\"\n", 279 | "df1['content'] = df1['content'].astype(str).apply(lambda x : x.replace(\"展开全文\", \"\"))\n", 280 | "# remove symbol, number and letter\n", 281 | "symbols = \"[a-zA-Z0-9\\s+\\.\\!\\/_,$%^*()??;；：【】+\\\"\\'\\[\\]\\\\]+|[+——！，;:。？《》、~@#￥%……&*（）“”.=-]+\"\n", 282 | "df1['content'] = df1['content'].astype(str).apply(lambda x : re.sub(symbols, '', x))" 283 | ] 284 | }, 285 | { 286 | "cell_type": "code", 287 | "execution_count": 41, 288 | "metadata": {}, 289 | "outputs": [ 290 | { 291 | "name": "stderr", 292 | "output_type": "stream", 293 | "text": [ 294 | "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", 295 | "A value is trying to be set on a copy of a slice from a DataFrame.\n", 296 | "Try using .loc[row_indexer,col_indexer] = value instead\n", 297 | "\n", 298 | "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", 299 | " \n" 300 | ] 301 | } 302 | ], 303 | "source": [ 304 | "pattern = re.compile(r'[^\\u4e00-\\u9fa5]')\n", 305 | "df1['content'] = df1['content'].astype(str).apply(lambda x: re.sub(pattern, '', x))" 306 | ] 307 | }, 308 | { 309 | "cell_type": "code", 310 | "execution_count": 42, 311 | "metadata": {}, 312 | "outputs": [ 313 | { 314 | "name": "stdout", 315 | "output_type": "stream", 316 | "text": [ 317 | "Model loaded succeed\n" 318 | ] 319 | }, 320 | { 321 | "name": "stderr", 322 | "output_type": "stream", 323 | "text": [ 324 | "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", 325 | "A value is trying to be set on a copy of a slice from a DataFrame.\n", 326 | "Try using .loc[row_indexer,col_indexer] = value instead\n", 327 | "\n", 328 | "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", 329 | " \n" 330 | ] 331 | } 332 | ], 333 | "source": [ 334 | "#tokenization using http://thulac.thunlp.org/\n", 335 | "thu1 = thulac.thulac(seg_only=True) #默认模式\n", 336 | "df1['content'] = df1['content'].astype(str).apply(lambda x : thu1.cut(x,text = True))" 337 | ] 338 | }, 339 | { 340 | "cell_type": "code", 341 | "execution_count": 43, 342 | "metadata": {}, 343 | "outputs": [ 344 | { 345 | "data": { 346 | "text/plain": [ 347 | "'写 在 年末 冬初 孩子 流感 的 第五 天 我们 仍然 没有 忘记 热情 拥抱 这 年 的 第一 天 带 着 一 丝 迷信 早晨 给 孩子 穿 上 红色 的 羽绒服 羽绒 裤祈祷 新 的 一 年 孩子 们 身体 康健 仍然 会 有 一 丝 焦虑 焦虑 我 的 孩子 为什 么 会 过早 的 懂事 从 两 岁 多 开始 关注 我 的 情绪 会 深沉 地 说 妈妈 你 终于 笑 了 这 句 话 像 刀子 一样 扎入 我'" 348 | ] 349 | }, 350 | "execution_count": 43, 351 | "metadata": {}, 352 | "output_type": "execute_result" 353 | } 354 | ], 355 | "source": [ 356 | "df1.content[0]" 357 | ] 358 | }, 359 | { 360 | "cell_type": "code", 361 | "execution_count": 44, 362 | "metadata": {}, 363 | "outputs": [], 364 | "source": [ 365 | "df1.to_csv(\"precleaned.csv\")" 366 | ] 367 | }, 368 | { 369 | "cell_type": "code", 370 | "execution_count": null, 371 | "metadata": {}, 372 | "outputs": [], 373 | "source": [ 374 | "myfile = './mystopwords.csv'\n", 375 | "stopwords = pd.read_csv(myfile, delimiter='\\n', header=None, encoding='utf-8', quoting=csv.QUOTE_NONE)\n", 376 | "stopwords.shape" 377 | ] 378 | }, 379 | { 380 | "cell_type": "code", 381 | "execution_count": null, 382 | "metadata": {}, 383 | "outputs": [], 384 | "source": [ 385 | "# thu1 = thulac.thulac(seg_only=True) #默认模式\n", 386 | "# df1 = df0\n", 387 | "# df1['content'] = df1['content'].astype(str).apply(lambda x : thu1.cut(x,text = True))\n", 388 | "# #text = thu1.cut(df0.content[0], text=True) #进行一句话分词\n", 389 | "# #print(text)\n", 390 | "# df1.head(10)" 391 | ] 392 | }, 393 | { 394 | "cell_type": "code", 395 | "execution_count": null, 396 | "metadata": {}, 397 | "outputs": [], 398 | "source": [ 399 | "count_vect = CountVectorizer(analyzer='word', token_pattern=r'\\w{1,}')\n", 400 | "count_vect.fit(df1['content'])\n", 401 | "xtrain_count = count_vect.transform(train_df['text_cut'])" 402 | ] 403 | } 404 | ], 405 | "metadata": { 406 | "kernelspec": { 407 | "display_name": "py_35_env", 408 | "language": "python", 409 | "name": "py_35_env" 410 | }, 411 | "language_info": { 412 | "codemirror_mode": { 413 | "name": "ipython", 414 | "version": 3 415 | }, 416 | "file_extension": ".py", 417 | "mimetype": "text/x-python", 418 | "name": "python", 419 | "nbconvert_exporter": "python", 420 | "pygments_lexer": "ipython3", 421 | "version": "3.5.4" 422 | } 423 | }, 424 | "nbformat": 4, 425 | "nbformat_minor": 4 426 | } 427 | -------------------------------------------------------------------------------- /SentimentAnalysis/stopwords.txt: -------------------------------------------------------------------------------- 1 | ! 2 | " 3 | # 4 | $ 5 | % 6 | & 7 | ' 8 | ( 9 | ) 10 | * 11 | + 12 | , 13 | - 14 | -- 15 | . 16 | .. 17 | ... 18 | ...... 19 | ................... 20 | ./ 21 | .一 22 | .数 23 | .日 24 | / 25 | // 26 | 0 27 | 1 28 | 2 29 | 3 30 | 4 31 | 5 32 | 6 33 | 7 34 | 8 35 | 9 36 | : 37 | :// 38 | :: 39 | ; 40 | < 41 | = 42 | > 43 | >> 44 | ? 45 | @ 46 | A 47 | Lex 48 | [ 49 | \ 50 | ] 51 | ^ 52 | _ 53 | ` 54 | exp 55 | sub 56 | sup 57 | | 58 | } 59 | ~ 60 | ~~~~ 61 | · 62 | × 63 | ××× 64 | Δ 65 | Ψ 66 | γ 67 | μ 68 | φ 69 | φ． 70 | В 71 | — 72 | —— 73 | ——— 74 | ‘ 75 | ’ 76 | ’‘ 77 | “ 78 | ” 79 | ”， 80 | … 81 | …… 82 | …………………………………………………③ 83 | ′∈ 84 | ′｜ 85 | ℃ 86 | Ⅲ 87 | ↑ 88 | → 89 | ∈［ 90 | ∪φ∈ 91 | ≈ 92 | ① 93 | ② 94 | ②ｃ 95 | ③ 96 | ③］ 97 | ④ 98 | ⑤ 99 | ⑥ 100 | ⑦ 101 | ⑧ 102 | ⑨ 103 | ⑩ 104 | ── 105 | ■ 106 | ▲ 107 | 　 108 | 、 109 | 。 110 | 〈 111 | 〉 112 | 《 113 | 》 114 | 》）， 115 | 」 116 | 『 117 | 』 118 | 【 119 | 】 120 | 〔 121 | 〕 122 | 〕〔 123 | ㈧ 124 | 一 125 | 一. 126 | 一一 127 | 一下 128 | 一个 129 | 一些 130 | 一何 131 | 一切 132 | 一则 133 | 一则通过 134 | 一天 135 | 一定 136 | 一方面 137 | 一旦 138 | 一时 139 | 一来 140 | 一样 141 | 一次 142 | 一片 143 | 一番 144 | 一直 145 | 一致 146 | 一般 147 | 一起 148 | 一转眼 149 | 一边 150 | 一面 151 | 七 152 | 万一 153 | 三 154 | 三天两头 155 | 三番两次 156 | 三番五次 157 | 上 158 | 上下 159 | 上升 160 | 上去 161 | 上来 162 | 上述 163 | 上面 164 | 下 165 | 下列 166 | 下去 167 | 下来 168 | 下面 169 | 不 170 | 不一 171 | 不下 172 | 不久 173 | 不了 174 | 不亦乐乎 175 | 不仅 176 | 不仅...而且 177 | 不仅仅 178 | 不仅仅是 179 | 不会 180 | 不但 181 | 不但...而且 182 | 不光 183 | 不免 184 | 不再 185 | 不力 186 | 不单 187 | 不变 188 | 不只 189 | 不可 190 | 不可开交 191 | 不可抗拒 192 | 不同 193 | 不外 194 | 不外乎 195 | 不够 196 | 不大 197 | 不如 198 | 不妨 199 | 不定 200 | 不对 201 | 不少 202 | 不尽 203 | 不尽然 204 | 不巧 205 | 不已 206 | 不常 207 | 不得 208 | 不得不 209 | 不得了 210 | 不得已 211 | 不必 212 | 不怎么 213 | 不怕 214 | 不惟 215 | 不成 216 | 不拘 217 | 不择手段 218 | 不敢 219 | 不料 220 | 不断 221 | 不日 222 | 不时 223 | 不是 224 | 不曾 225 | 不止 226 | 不止一次 227 | 不比 228 | 不消 229 | 不满 230 | 不然 231 | 不然的话 232 | 不特 233 | 不独 234 | 不由得 235 | 不知不觉 236 | 不管 237 | 不管怎样 238 | 不经意 239 | 不胜 240 | 不能 241 | 不能不 242 | 不至于 243 | 不若 244 | 不要 245 | 不论 246 | 不起 247 | 不足 248 | 不过 249 | 不迭 250 | 不问 251 | 不限 252 | 与 253 | 与其 254 | 与其说 255 | 与否 256 | 与此同时 257 | 专门 258 | 且 259 | 且不说 260 | 且说 261 | 两者 262 | 严格 263 | 严重 264 | 个 265 | 个人 266 | 个别 267 | 中小 268 | 中间 269 | 丰富 270 | 串行 271 | 临 272 | 临到 273 | 为 274 | 为主 275 | 为了 276 | 为什么 277 | 为什麽 278 | 为何 279 | 为止 280 | 为此 281 | 为着 282 | 主张 283 | 主要 284 | 举凡 285 | 举行 286 | 乃 287 | 乃至 288 | 乃至于 289 | 么 290 | 之 291 | 之一 292 | 之前 293 | 之后 294 | 之後 295 | 之所以 296 | 之类 297 | 乌乎 298 | 乎 299 | 乒 300 | 乘 301 | 乘势 302 | 乘机 303 | 乘胜 304 | 乘虚 305 | 乘隙 306 | 九 307 | 也 308 | 也好 309 | 也就是说 310 | 也是 311 | 也罢 312 | 了 313 | 了解 314 | 争取 315 | 二 316 | 二来 317 | 二话不说 318 | 二话没说 319 | 于 320 | 于是 321 | 于是乎 322 | 云云 323 | 云尔 324 | 互 325 | 互相 326 | 五 327 | 些 328 | 交口 329 | 亦 330 | 产生 331 | 亲口 332 | 亲手 333 | 亲眼 334 | 亲自 335 | 亲身 336 | 人 337 | 人人 338 | 人们 339 | 人家 340 | 人民 341 | 什么 342 | 什么样 343 | 什麽 344 | 仅 345 | 仅仅 346 | 今 347 | 今后 348 | 今天 349 | 今年 350 | 今後 351 | 介于 352 | 仍 353 | 仍旧 354 | 仍然 355 | 从 356 | 从不 357 | 从严 358 | 从中 359 | 从事 360 | 从今以后 361 | 从优 362 | 从古到今 363 | 从古至今 364 | 从头 365 | 从宽 366 | 从小 367 | 从新 368 | 从无到有 369 | 从早到晚 370 | 从未 371 | 从来 372 | 从此 373 | 从此以后 374 | 从而 375 | 从轻 376 | 从速 377 | 从重 378 | 他 379 | 他人 380 | 他们 381 | 他是 382 | 他的 383 | 代替 384 | 以 385 | 以上 386 | 以下 387 | 以为 388 | 以便 389 | 以免 390 | 以前 391 | 以及 392 | 以后 393 | 以外 394 | 以後 395 | 以故 396 | 以期 397 | 以来 398 | 以至 399 | 以至于 400 | 以致 401 | 们 402 | 任 403 | 任何 404 | 任凭 405 | 任务 406 | 企图 407 | 伙同 408 | 会 409 | 伟大 410 | 传 411 | 传说 412 | 传闻 413 | 似乎 414 | 似的 415 | 但 416 | 但凡 417 | 但愿 418 | 但是 419 | 何 420 | 何乐而不为 421 | 何以 422 | 何况 423 | 何处 424 | 何妨 425 | 何尝 426 | 何必 427 | 何时 428 | 何止 429 | 何苦 430 | 何须 431 | 余外 432 | 作为 433 | 你 434 | 你们 435 | 你是 436 | 你的 437 | 使 438 | 使得 439 | 使用 440 | 例如 441 | 依 442 | 依据 443 | 依照 444 | 依靠 445 | 便 446 | 便于 447 | 促进 448 | 保持 449 | 保管 450 | 保险 451 | 俺 452 | 俺们 453 | 倍加 454 | 倍感 455 | 倒不如 456 | 倒不如说 457 | 倒是 458 | 倘 459 | 倘使 460 | 倘或 461 | 倘然 462 | 倘若 463 | 借 464 | 借以 465 | 借此 466 | 假使 467 | 假如 468 | 假若 469 | 偏偏 470 | 做到 471 | 偶尔 472 | 偶而 473 | 傥然 474 | 像 475 | 儿 476 | 允许 477 | 元／吨 478 | 充其极 479 | 充其量 480 | 充分 481 | 先不先 482 | 先后 483 | 先後 484 | 先生 485 | 光 486 | 光是 487 | 全体 488 | 全力 489 | 全年 490 | 全然 491 | 全身心 492 | 全部 493 | 全都 494 | 全面 495 | 八 496 | 八成 497 | 公然 498 | 六 499 | 兮 500 | 共 501 | 共同 502 | 共总 503 | 关于 504 | 其 505 | 其一 506 | 其中 507 | 其二 508 | 其他 509 | 其余 510 | 其后 511 | 其它 512 | 其实 513 | 其次 514 | 具体 515 | 具体地说 516 | 具体来说 517 | 具体说来 518 | 具有 519 | 兼之 520 | 内 521 | 再 522 | 再其次 523 | 再则 524 | 再有 525 | 再次 526 | 再者 527 | 再者说 528 | 再说 529 | 冒 530 | 冲 531 | 决不 532 | 决定 533 | 决非 534 | 况且 535 | 准备 536 | 凑巧 537 | 凝神 538 | 几 539 | 几乎 540 | 几度 541 | 几时 542 | 几番 543 | 几经 544 | 凡 545 | 凡是 546 | 凭 547 | 凭借 548 | 出 549 | 出于 550 | 出去 551 | 出来 552 | 出现 553 | 分别 554 | 分头 555 | 分期 556 | 分期分批 557 | 切 558 | 切不可 559 | 切切 560 | 切勿 561 | 切莫 562 | 则 563 | 则甚 564 | 刚 565 | 刚好 566 | 刚巧 567 | 刚才 568 | 初 569 | 别 570 | 别人 571 | 别处 572 | 别是 573 | 别的 574 | 别管 575 | 别说 576 | 到 577 | 到了儿 578 | 到处 579 | 到头 580 | 到头来 581 | 到底 582 | 到目前为止 583 | 前后 584 | 前此 585 | 前者 586 | 前进 587 | 前面 588 | 加上 589 | 加之 590 | 加以 591 | 加入 592 | 加强 593 | 动不动 594 | 动辄 595 | 勃然 596 | 匆匆 597 | 十分 598 | 千 599 | 千万 600 | 千万千万 601 | 半 602 | 单 603 | 单单 604 | 单纯 605 | 即 606 | 即令 607 | 即使 608 | 即便 609 | 即刻 610 | 即如 611 | 即将 612 | 即或 613 | 即是说 614 | 即若 615 | 却 616 | 却不 617 | 历 618 | 原来 619 | 去 620 | 又 621 | 又及 622 | 及 623 | 及其 624 | 及时 625 | 及至 626 | 双方 627 | 反之 628 | 反之亦然 629 | 反之则 630 | 反倒 631 | 反倒是 632 | 反应 633 | 反手 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1171 | 暗中 1172 | 暗地里 1173 | 暗自 1174 | 更 1175 | 更为 1176 | 更加 1177 | 更进一步 1178 | 曾 1179 | 曾经 1180 | 替 1181 | 替代 1182 | 最 1183 | 最后 1184 | 最大 1185 | 最好 1186 | 最後 1187 | 最近 1188 | 最高 1189 | 有 1190 | 有些 1191 | 有关 1192 | 有利 1193 | 有力 1194 | 有及 1195 | 有所 1196 | 有效 1197 | 有时 1198 | 有点 1199 | 有的 1200 | 有的是 1201 | 有着 1202 | 有著 1203 | 望 1204 | 朝 1205 | 朝着 1206 | 末##末 1207 | 本 1208 | 本人 1209 | 本地 1210 | 本着 1211 | 本身 1212 | 权时 1213 | 来 1214 | 来不及 1215 | 来得及 1216 | 来看 1217 | 来着 1218 | 来自 1219 | 来讲 1220 | 来说 1221 | 极 1222 | 极为 1223 | 极了 1224 | 极其 1225 | 极力 1226 | 极大 1227 | 极度 1228 | 极端 1229 | 构成 1230 | 果然 1231 | 果真 1232 | 某 1233 | 某个 1234 | 某些 1235 | 某某 1236 | 根据 1237 | 根本 1238 | 格外 1239 | 梆 1240 | 概 1241 | 次第 1242 | 欢迎 1243 | 欤 1244 | 正值 1245 | 正在 1246 | 正如 1247 | 正巧 1248 | 正常 1249 | 正是 1250 | 此 1251 | 此中 1252 | 此后 1253 | 此地 1254 | 此处 1255 | 此外 1256 | 此时 1257 | 此次 1258 | 此间 1259 | 殆 1260 | 毋宁 1261 | 每 1262 | 每个 1263 | 每天 1264 | 每年 1265 | 每当 1266 | 每时每刻 1267 | 每每 1268 | 每逢 1269 | 比 1270 | 比及 1271 | 比如 1272 | 比如说 1273 | 比方 1274 | 比照 1275 | 比起 1276 | 比较 1277 | 毕竟 1278 | 毫不 1279 | 毫无 1280 | 毫无例外 1281 | 毫无保留地 1282 | 汝 1283 | 沙沙 1284 | 没 1285 | 没奈何 1286 | 没有 1287 | 沿 1288 | 沿着 1289 | 注意 1290 | 活 1291 | 深入 1292 | 清楚 1293 | 满 1294 | 满足 1295 | 漫说 1296 | 焉 1297 | 然 1298 | 然则 1299 | 然后 1300 | 然後 1301 | 然而 1302 | 照 1303 | 照着 1304 | 牢牢 1305 | 特别是 1306 | 特殊 1307 | 特点 1308 | 犹且 1309 | 犹自 1310 | 独 1311 | 独自 1312 | 猛然 1313 | 猛然间 1314 | 率尔 1315 | 率然 1316 | 现代 1317 | 现在 1318 | 理应 1319 | 理当 1320 | 理该 1321 | 瑟瑟 1322 | 甚且 1323 | 甚么 1324 | 甚或 1325 | 甚而 1326 | 甚至 1327 | 甚至于 1328 | 用 1329 | 用来 1330 | 甫 1331 | 甭 1332 | 由 1333 | 由于 1334 | 由是 1335 | 由此 1336 | 由此可见 1337 | 略 1338 | 略为 1339 | 略加 1340 | 略微 1341 | 白 1342 | 白白 1343 | 的 1344 | 的确 1345 | 的话 1346 | 皆可 1347 | 目前 1348 | 直到 1349 | 直接 1350 | 相似 1351 | 相信 1352 | 相反 1353 | 相同 1354 | 相对 1355 | 相对而言 1356 | 相应 1357 | 相当 1358 | 相等 1359 | 省得 1360 | 看 1361 | 看上去 1362 | 看出 1363 | 看到 1364 | 看来 1365 | 看样子 1366 | 看看 1367 | 看见 1368 | 看起来 1369 | 真是 1370 | 真正 1371 | 眨眼 1372 | 着 1373 | 着呢 1374 | 矣 1375 | 矣乎 1376 | 矣哉 1377 | 知道 1378 | 砰 1379 | 确定 1380 | 碰巧 1381 | 社会主义 1382 | 离 1383 | 种 1384 | 积极 1385 | 移动 1386 | 究竟 1387 | 穷年累月 1388 | 突出 1389 | 突然 1390 | 窃 1391 | 立 1392 | 立刻 1393 | 立即 1394 | 立地 1395 | 立时 1396 | 立马 1397 | 竟 1398 | 竟然 1399 | 竟而 1400 | 第 1401 | 第二 1402 | 等 1403 | 等到 1404 | 等等 1405 | 策略地 1406 | 简直 1407 | 简而言之 1408 | 简言之 1409 | 管 1410 | 类如 1411 | 粗 1412 | 精光 1413 | 紧接着 1414 | 累年 1415 | 累次 1416 | 纯 1417 | 纯粹 1418 | 纵 1419 | 纵令 1420 | 纵使 1421 | 纵然 1422 | 练习 1423 | 组成 1424 | 经 1425 | 经常 1426 | 经过 1427 | 结合 1428 | 结果 1429 | 给 1430 | 绝 1431 | 绝不 1432 | 绝对 1433 | 绝非 1434 | 绝顶 1435 | 继之 1436 | 继后 1437 | 继续 1438 | 继而 1439 | 维持 1440 | 综上所述 1441 | 缕缕 1442 | 罢了 1443 | 老 1444 | 老大 1445 | 老是 1446 | 老老实实 1447 | 考虑 1448 | 者 1449 | 而 1450 | 而且 1451 | 而况 1452 | 而又 1453 | 而后 1454 | 而外 1455 | 而已 1456 | 而是 1457 | 而言 1458 | 而论 1459 | 联系 1460 | 联袂 1461 | 背地里 1462 | 背靠背 1463 | 能 1464 | 能否 1465 | 能够 1466 | 腾 1467 | 自 1468 | 自个儿 1469 | 自从 1470 | 自各儿 1471 | 自后 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| 转贴 1574 | 轰然 1575 | 较 1576 | 较为 1577 | 较之 1578 | 较比 1579 | 边 1580 | 达到 1581 | 达旦 1582 | 迄 1583 | 迅速 1584 | 过 1585 | 过于 1586 | 过去 1587 | 过来 1588 | 运用 1589 | 近 1590 | 近几年来 1591 | 近年来 1592 | 近来 1593 | 还 1594 | 还是 1595 | 还有 1596 | 还要 1597 | 这 1598 | 这一来 1599 | 这个 1600 | 这么 1601 | 这么些 1602 | 这么样 1603 | 这么点儿 1604 | 这些 1605 | 这会儿 1606 | 这儿 1607 | 这就是说 1608 | 这时 1609 | 这样 1610 | 这次 1611 | 这点 1612 | 这种 1613 | 这般 1614 | 这边 1615 | 这里 1616 | 这麽 1617 | 进入 1618 | 进去 1619 | 进来 1620 | 进步 1621 | 进而 1622 | 进行 1623 | 连 1624 | 连同 1625 | 连声 1626 | 连日 1627 | 连日来 1628 | 连袂 1629 | 连连 1630 | 迟早 1631 | 迫于 1632 | 适应 1633 | 适当 1634 | 适用 1635 | 逐步 1636 | 逐渐 1637 | 通常 1638 | 通过 1639 | 造成 1640 | 逢 1641 | 遇到 1642 | 遭到 1643 | 遵循 1644 | 遵照 1645 | 避免 1646 | 那 1647 | 那个 1648 | 那么 1649 | 那么些 1650 | 那么样 1651 | 那些 1652 | 那会儿 1653 | 那儿 1654 | 那时 1655 | 那末 1656 | 那样 1657 | 那般 1658 | 那边 1659 | 那里 1660 | 那麽 1661 | 部分 1662 | 都 1663 | 鄙人 1664 | 采取 1665 | 里面 1666 | 重大 1667 | 重新 1668 | 重要 1669 | 鉴于 1670 | 针对 1671 | 长期以来 1672 | 长此下去 1673 | 长线 1674 | 长话短说 1675 | 问题 1676 | 间或 1677 | 防止 1678 | 阿 1679 | 附近 1680 | 陈年 1681 | 限制 1682 | 陡然 1683 | 除 1684 | 除了 1685 | 除却 1686 | 除去 1687 | 除外 1688 | 除开 1689 | 除此 1690 | 除此之外 1691 | 除此以外 1692 | 除此而外 1693 | 除非 1694 | 随 1695 | 随后 1696 | 随时 1697 | 随着 1698 | 随著 1699 | 隔夜 1700 | 隔日 1701 | 难得 1702 | 难怪 1703 | 难说 1704 | 难道 1705 | 难道说 1706 | 集中 1707 | 零 1708 | 需要 1709 | 非但 1710 | 非常 1711 | 非徒 1712 | 非得 1713 | 非特 1714 | 非独 1715 | 靠 1716 | 顶多 1717 | 顷 1718 | 顷刻 1719 | 顷刻之间 1720 | 顷刻间 1721 | 顺 1722 | 顺着 1723 | 顿时 1724 | 颇 1725 | 风雨无阻 1726 | 饱 1727 | 首先 1728 | 马上 1729 | 高低 1730 | 高兴 1731 | 默然 1732 | 默默地 1733 | 齐 1734 | ︿ 1735 | ！ 1736 | ＃ 1737 | ＄ 1738 | ％ 1739 | ＆ 1740 | ＇ 1741 | （ 1742 | ） 1743 | ）÷（１－ 1744 | ）、 1745 | ＊ 1746 | ＋ 1747 | ＋ξ 1748 | ＋＋ 1749 | ， 1750 | ，也 1751 | － 1752 | －β 1753 | －－ 1754 | －［＊］－ 1755 | ． 1756 | ／ 1757 | ０ 1758 | ０：２ 1759 | １ 1760 | １． 1761 | １２％ 1762 | ２ 1763 | ２．３％ 1764 | ３ 1765 | ４ 1766 | ５ 1767 | ５：０ 1768 | ６ 1769 | ７ 1770 | ８ 1771 | ９ 1772 | ： 1773 | ； 1774 | ＜ 1775 | ＜± 1776 | ＜Δ 1777 | ＜λ 1778 | ＜φ 1779 | ＜＜ 1780 | ＝ 1781 | ＝″ 1782 | ＝☆ 1783 | ＝（ 1784 | ＝－ 1785 | ＝［ 1786 | ＝｛ 1787 | ＞ 1788 | ＞λ 1789 | ？ 1790 | ＠ 1791 | Ａ 1792 | ＬＩ 1793 | Ｒ．Ｌ． 1794 | ＺＸＦＩＴＬ 1795 | ［ 1796 | ［①①］ 1797 | ［①②］ 1798 | ［①③］ 1799 | ［①④］ 1800 | ［①⑤］ 1801 | ［①⑥］ 1802 | ［①⑦］ 1803 | ［①⑧］ 1804 | ［①⑨］ 1805 | ［①Ａ］ 1806 | ［①Ｂ］ 1807 | ［①Ｃ］ 1808 | ［①Ｄ］ 1809 | ［①Ｅ］ 1810 | ［①］ 1811 | ［①ａ］ 1812 | ［①ｃ］ 1813 | ［①ｄ］ 1814 | ［①ｅ］ 1815 | ［①ｆ］ 1816 | ［①ｇ］ 1817 | ［①ｈ］ 1818 | ［①ｉ］ 1819 | ［①ｏ］ 1820 | ［② 1821 | ［②①］ 1822 | ［②②］ 1823 | ［②③］ 1824 | ［②④ 1825 | ［②⑤］ 1826 | ［②⑥］ 1827 | ［②⑦］ 1828 | ［②⑧］ 1829 | ［②⑩］ 1830 | ［②Ｂ］ 1831 | ［②Ｇ］ 1832 | ［②］ 1833 | ［②ａ］ 1834 | ［②ｂ］ 1835 | ［②ｃ］ 1836 | ［②ｄ］ 1837 | ［②ｅ］ 1838 | ［②ｆ］ 1839 | ［②ｇ］ 1840 | ［②ｈ］ 1841 | ［②ｉ］ 1842 | ［②ｊ］ 1843 | ［③①］ 1844 | ［③⑩］ 1845 | ［③Ｆ］ 1846 | ［③］ 1847 | ［③ａ］ 1848 | ［③ｂ］ 1849 | ［③ｃ］ 1850 | ［③ｄ］ 1851 | ［③ｅ］ 1852 | ［③ｇ］ 1853 | ［③ｈ］ 1854 | ［④］ 1855 | ［④ａ］ 1856 | ［④ｂ］ 1857 | ［④ｃ］ 1858 | ［④ｄ］ 1859 | ［④ｅ］ 1860 | ［⑤］ 1861 | ［⑤］］ 1862 | ［⑤ａ］ 1863 | ［⑤ｂ］ 1864 | ［⑤ｄ］ 1865 | ［⑤ｅ］ 1866 | ［⑤ｆ］ 1867 | ［⑥］ 1868 | ［⑦］ 1869 | ［⑧］ 1870 | ［⑨］ 1871 | ［⑩］ 1872 | ［＊］ 1873 | ［－ 1874 | ［］ 1875 | ］ 1876 | ］∧′＝［ 1877 | ］［ 1878 | ＿ 1879 | ａ］ 1880 | ｂ］ 1881 | ｃ］ 1882 | ｅ］ 1883 | ｆ］ 1884 | ｎｇ昉 1885 | ｛ 1886 | ｛－ 1887 | ｜ 1888 | ｝ 1889 | ｝＞ 1890 | ～ 1891 | ～± 1892 | ～＋ 1893 | ￥ 1894 | --------------------------------------------------------------------------------