1_tweet_collect_zh.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Emoji Sentiment Analysis with Tweets_zh\n",
    "        \n",
    "## step1-Data collection\n",
    "1. querry for data from twitter api\n",
    "    - period: 2022-0415-2022-0528\n",
    "    - filter: retweet or media\n",
    "    - querry:tweeets that contain at least one concerned emoji\n",
    "\n",
    "![](https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcTigQWzoYCNiDyrz1BN4WTf2X2k9OZ_yvW-FsmcIMsdS9fppNmh)\n",
    "\n",
    "In this section, we refer to [Ian D. Wood, Sebastian Ruder, 2016](https://www.researchgate.net/publication/321057905_Emoji_as_Emotion_Tags_for_Tweets) to choose commonly used emojis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/leahtan/Documents/3_study/self_study/Python/2022_spring_CTA/project/code'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ! pip install Tweepy\n",
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! cd /Users/leahtan/Documents/3_study/self_study/Python/2022_spring_CTA/project/code"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scraping Chinese Tweets with emojis\n",
    "\n",
    "### step 1.1: construct a list of common emojis\n",
    "![image-2.png](attachment:image-2.png)\n",
    " \n",
    "### step 1.2: def querry\n",
    "\n",
    "### step 1.3: scraping and save to csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------\n",
    "## 1.1, list of common emojis\n",
    "In this section, we refer to [Ian D. Wood, Sebastian Ruder, 2016](https://www.researchgate.net/publication/321057905_Emoji_as_Emotion_Tags_for_Tweets) to choose commonly used emojis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['😀', '😂', '😃', '😄', '😆', '😇', '😉', '😊', '😋', '😌', '😍', '😎', '😏', '🌞', '☺', '😘', '😜', '😝', '😛', '😺', '😸', '😹', '😻', '😼', '❤', '💖', '💕', '😁', '♥', '😬', '😠', '😐', '😑', '😠', '😡', '😖', '😤', '😾', '💩', '😅', '😦', '😧', '😱', '😨', '😰', '🙀', '😔', '😕', '☹', '😫', '😩', '😢', '😥', '😪', '😓', '😭', '😿', '💔', '😳', '😯', '😵', '😲']\n",
      "62\n"
     ]
    }
   ],
   "source": [
    "\n",
    "joy = ['\\U0001F600', '\\U0001F602', '\\U0001F603', '\\U0001F604',\n",
    "          '\\U0001F606', '\\U0001F607', '\\U0001F609', '\\U0001F60A',\n",
    "          '\\U0001F60B', '\\U0001F60C', '\\U0001F60D', '\\U0001F60E',\n",
    "          '\\U0001F60F', '\\U0001F31E', '\\U0000263A', '\\U0001F618',\n",
    "          '\\U0001F61C', '\\U0001F61D', '\\U0001F61B', '\\U0001F63A',\n",
    "          '\\U0001F638', '\\U0001F639', '\\U0001F63B', '\\U0001F63C',\n",
    "          '\\U00002764', '\\U0001F496', '\\U0001F495', '\\U0001F601',\n",
    "          '\\U00002665']#joy\n",
    "\n",
    "anger = ['\\U0001F62C', '\\U0001F620', '\\U0001F610',\n",
    "          '\\U0001F611', '\\U0001F620', '\\U0001F621', '\\U0001F616',\n",
    "          '\\U0001F624', '\\U0001F63E']#anger\n",
    "disgust= ['\\U0001F4A9']#disgust\n",
    "fear = ['\\U0001F605', '\\U0001F626', '\\U0001F627', '\\U0001F631',\n",
    "          '\\U0001F628', '\\U0001F630', '\\U0001F640']#fear\n",
    "sad = ['\\U0001F614', '\\U0001F615', '\\U00002639', '\\U0001F62B',\n",
    "          '\\U0001F629', '\\U0001F622', '\\U0001F625', '\\U0001F62A',\n",
    "          '\\U0001F613', '\\U0001F62D', '\\U0001F63F', '\\U0001F494']#sad\n",
    "surp = ['\\U0001F633', '\\U0001F62F', '\\U0001F635', '\\U0001F632']#surprise\n",
    "\n",
    "emojis = joy + anger + disgust + fear + sad + surp\n",
    "print(emojis)\n",
    "print(len(emojis))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2. define querry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[467741, 145161, 16129, 112903, 193548, 64516]\n"
     ]
    }
   ],
   "source": [
    "# decide the scrabing \n",
    "# there is a limitation in twitter api per month\n",
    "# we are about to firstly craw approximately 100,000 Tweets. \n",
    "# To balance the sample, we calculate the required scale of each group separately\n",
    "# The weight is the number of emojis containted in each group\n",
    "sum = 1000000\n",
    "list = [joy, anger, disgust, fear, sad, surp]\n",
    "maxquerry = []\n",
    "for group in list:\n",
    "    sample = int((sum/62)*len(group))\n",
    "    maxquerry.append(sample)\n",
    "print(maxquerry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = '2022-05-26'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Twitter API Authentication\n",
    "import tweepy\n",
    "consumer_key = 'B1HITEZpHcS4isYU492RaBKcu'\n",
    "consumer_secret = 'qiO3lHo2VECACt50My2Y6eozkliAiEMkbD967MA9BFmDhD3Z4l'\n",
    "access_token = '1355036933823541248-PAxS6qX9CqpmB2wVHDcmIcmmM9fnZa'\n",
    "access_token_secret = 'vcH5ikw31xpyaqPzCCBy5Rf0y2Et6gdZsLFhM5SVUUbe4'\n",
    "\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "api = tweepy.API(auth,wait_on_rate_limit=True)\n",
    "\n",
    "# api = tweepy.API(auth, proxy='http://127.0.0.1:7890',wait_on_rate_limit=True)#proxy and automatically set timeout\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3. crawing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-27-026230b2e863>:2: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('tweets_zh.csv',converters={'id':str})\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('tweets_zh.csv',converters={'id':str})\n",
    "df = df[df['id'] != 'id']\n",
    "df = df.drop_duplicates()\n",
    "# lastid = df['id'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'😀 OR 😂 OR 😃 OR 😄 OR 😆 OR 😇 OR 😉 OR 😊 OR 😋 OR 😌 OR 😍 OR 😎 OR 😏 OR 🌞 OR ☺ OR 😘 OR 😜 OR 😝 OR 😛 OR 😺 OR 😸 OR 😹 OR 😻 OR 😼 OR ❤ OR 💖 OR 💕 OR 😁 OR ♥'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define the search request keywords\n",
    "# since the URL-encoded search query of 500 characters maximum, we seperately send the requests\n",
    "\n",
    "# 1-joy related emojis:\n",
    "keywords = ' OR '.join(joy)\n",
    "keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this stream of emoji, we collect 467741 Tweets\n",
      "For each day in the past week, we collect 66820 Tweets\n"
     ]
    }
   ],
   "source": [
    "max_tweets = maxquerry[0]\n",
    "count = int(max_tweets/7)\n",
    "print('For this stream of emoji, we collect {} Tweets'.format(max_tweets))\n",
    "print('For each day in the past week, we collect {} Tweets'.format(count))\n",
    "lastid = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Twritter api: <tweepy.api.API object at 0x7fa644d72dc0>\n",
      "Search for Tweets containing at least one of the following emoji 😀 OR 😂 OR 😃 OR 😄 OR 😆 OR 😇 OR 😉 OR 😊 OR 😋 OR 😌 OR 😍 OR 😎 OR 😏 OR 🌞 OR ☺ OR 😘 OR 😜 OR 😝 OR 😛 OR 😺 OR 😸 OR 😹 OR 😻 OR 😼 OR ❤ OR 💖 OR 💕 OR 😁 OR ♥-filter:retweets-filter:media\n",
      "Something wrong!\n",
      "The last Tweet: 2022-05-23 23:54:48+00:00 @andy37732083 @lovewing0710 人家就沒有在新竹啊😂\n",
      "The last id: \n"
     ]
    }
   ],
   "source": [
    "import tweepy\n",
    "import csv\n",
    "t = 'joy'\n",
    "\n",
    "print ('Twritter api:', api)\n",
    "# at least contains one relevant emoji \n",
    "#filting retweets and tweets with media\n",
    "query = keywords +'-filter:retweets'+'-filter:media' \n",
    "print ('Search for Tweets containing at least one of the following emoji', query)\n",
    "\n",
    "csvFile = open('tweets_zh.csv', 'a')\n",
    "csvWriter = csv.writer(csvFile)\n",
    "header = ['id','time', 'tweets','source','entities','retweet','favorite','type']\n",
    "\n",
    "if lastid == '':\n",
    "    csvWriter.writerow(header) #write header to csv\n",
    "    try: \n",
    "        search = tweepy.Cursor(api.search_tweets, \n",
    "                          q=query,\n",
    "                          count=count,\n",
    "                          result_type='recent',#Include both popular and real time results in the response.\n",
    "                          lang='zh',                               \n",
    "                          until=date).items()\n",
    "        for status in search:\n",
    "            lastid = status.id\n",
    "            if len(status.text)>1:\n",
    "                csvWriter.writerow([status.id, status.created_at, status.text, \n",
    "                                    status.source, status.entities, status.retweet_count, \n",
    "                                    status.favorite_count, t])#save tweets to csv\n",
    "    except:\n",
    "        print('Something wrong!')\n",
    "        print('The last Tweet:', status.created_at, status.text)\n",
    "    print('The last id:', lastid)\n",
    "\n",
    "else:\n",
    "    try: \n",
    "        search = tweepy.Cursor(api.search_tweets, \n",
    "                               q=query,\n",
    "                              count=count,\n",
    "                              result_type='recent',\n",
    "                              lang='zh',\n",
    "                               until=date,\n",
    "                              max_id=lastid).items()\n",
    "        # the bigger the id is, the older the Tweet is. \n",
    "        # max_id setting means the scraped Tweets is older than the the oldest one we have so far\n",
    "        for status in search:\n",
    "            lastid = status.id\n",
    "            if len(status.text)>1:\n",
    "                csvWriter.writerow([status.id, status.created_at, status.text, \n",
    "                                    status.source, status.entities, status.retweet_count, \n",
    "                                    status.favorite_count, t])#save tweets to csv\n",
    "    except:\n",
    "        print('Something wrong!')\n",
    "        print('The last Tweet:', status.created_at, status.text)\n",
    "    print('The last id:', lastid)\n",
    "\n",
    "csvFile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-f04b5c11189e>:1: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('tweets_zh.csv',converters={'id':str},index_col=0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>time</th>\n",
       "      <th>tweets</th>\n",
       "      <th>source</th>\n",
       "      <th>entities</th>\n",
       "      <th>retweet</th>\n",
       "      <th>favorite</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1515279424953876481</td>\n",
       "      <td>2022-04-16 10:42:22+00:00</td>\n",
       "      <td>BOY STORY 默契大挑战 EP.02\\n\\n▶️https://t.co/G5T5Mc...</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>131</td>\n",
       "      <td>712</td>\n",
       "      <td>joy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1516204930461159425</td>\n",
       "      <td>2022-04-18 23:59:59+00:00</td>\n",
       "      <td>@QFsFP3yAZKKFBwK 节省网络存储空间？😂</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>joy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1516204918977470467</td>\n",
       "      <td>2022-04-18 23:59:57+00:00</td>\n",
       "      <td>RT @Baoliaogeming64: 一部手机拯救了乌克兰战士的生命——一颗 7,62 ...</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>joy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1516204916217311232</td>\n",
       "      <td>2022-04-18 23:59:56+00:00</td>\n",
       "      <td>RT @S7i5FV0JOz6sV3A: 去tmd病毒！泰国泼水节好开心😃 https://...</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>joy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1516204913818501120</td>\n",
       "      <td>2022-04-18 23:59:55+00:00</td>\n",
       "      <td>RT @Fang961103: 沒人就脫一下\\n影片有人想看嗎😊 https://t.co/...</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>joy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528887200066129920</th>\n",
       "      <td>2022-05-23 23:54:48+00:00</td>\n",
       "      <td>@andy37732083 @lovewing0710 人家就沒有在新竹啊😂</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>joy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528888184850878464</th>\n",
       "      <td>2022-05-23 23:58:43+00:00</td>\n",
       "      <td>RT @sk55688: 💛💜ABO趴囉，畫個Alpha易感期塗鴉，喜歡AxB，A易感期到了...</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>{'hashtags': [{'text': 'lucashu', 'indices': [...</td>\n",
       "      <td>594</td>\n",
       "      <td>0</td>\n",
       "      <td>joy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528888106589765633</th>\n",
       "      <td>2022-05-23 23:58:24+00:00</td>\n",
       "      <td>@Kevin52uk @Haruo_middle @MatchaDark 哇～～～15歲！現...</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>joy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528888020786479104</th>\n",
       "      <td>2022-05-23 23:58:04+00:00</td>\n",
       "      <td>RT @wintergreen_tsc: 争取与期待已久的功能终于上线了💪🏻 简体中文和繁体...</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>{'hashtags': [{'text': 'ApeSwap', 'indices': [...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>joy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528887936325779456</th>\n",
       "      <td>2022-05-23 23:57:44+00:00</td>\n",
       "      <td>@taohuayicu 应该很少人知道勒巴骨就是肋骨😂😂</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>joy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184725 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            id  \\\n",
       "0                          1515279424953876481   \n",
       "1                          1516204930461159425   \n",
       "2                          1516204918977470467   \n",
       "3                          1516204916217311232   \n",
       "4                          1516204913818501120   \n",
       "...                                        ...   \n",
       "1528887200066129920  2022-05-23 23:54:48+00:00   \n",
       "1528888184850878464  2022-05-23 23:58:43+00:00   \n",
       "1528888106589765633  2022-05-23 23:58:24+00:00   \n",
       "1528888020786479104  2022-05-23 23:58:04+00:00   \n",
       "1528887936325779456  2022-05-23 23:57:44+00:00   \n",
       "\n",
       "                                                                  time  \\\n",
       "0                                            2022-04-16 10:42:22+00:00   \n",
       "1                                            2022-04-18 23:59:59+00:00   \n",
       "2                                            2022-04-18 23:59:57+00:00   \n",
       "3                                            2022-04-18 23:59:56+00:00   \n",
       "4                                            2022-04-18 23:59:55+00:00   \n",
       "...                                                                ...   \n",
       "1528887200066129920             @andy37732083 @lovewing0710 人家就沒有在新竹啊😂   \n",
       "1528888184850878464  RT @sk55688: 💛💜ABO趴囉，畫個Alpha易感期塗鴉，喜歡AxB，A易感期到了...   \n",
       "1528888106589765633  @Kevin52uk @Haruo_middle @MatchaDark 哇～～～15歲！現...   \n",
       "1528888020786479104  RT @wintergreen_tsc: 争取与期待已久的功能终于上线了💪🏻 简体中文和繁体...   \n",
       "1528887936325779456                       @taohuayicu 应该很少人知道勒巴骨就是肋骨😂😂   \n",
       "\n",
       "                                                                tweets  \\\n",
       "0                    BOY STORY 默契大挑战 EP.02\\n\\n▶️https://t.co/G5T5Mc...   \n",
       "1                                          @QFsFP3yAZKKFBwK 节省网络存储空间？😂   \n",
       "2                    RT @Baoliaogeming64: 一部手机拯救了乌克兰战士的生命——一颗 7,62 ...   \n",
       "3                    RT @S7i5FV0JOz6sV3A: 去tmd病毒！泰国泼水节好开心😃 https://...   \n",
       "4                    RT @Fang961103: 沒人就脫一下\\n影片有人想看嗎😊 https://t.co/...   \n",
       "...                                                                ...   \n",
       "1528887200066129920                                 Twitter for iPhone   \n",
       "1528888184850878464                                Twitter for Android   \n",
       "1528888106589765633                                 Twitter for iPhone   \n",
       "1528888020786479104                                    Twitter Web App   \n",
       "1528887936325779456                                Twitter for Android   \n",
       "\n",
       "                                                                source  \\\n",
       "0                                                   Twitter for iPhone   \n",
       "1                                                  Twitter for Android   \n",
       "2                                                      Twitter Web App   \n",
       "3                                                  Twitter for Android   \n",
       "4                                                  Twitter for Android   \n",
       "...                                                                ...   \n",
       "1528887200066129920  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "1528888184850878464  {'hashtags': [{'text': 'lucashu', 'indices': [...   \n",
       "1528888106589765633  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "1528888020786479104  {'hashtags': [{'text': 'ApeSwap', 'indices': [...   \n",
       "1528887936325779456  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "\n",
       "                                                              entities  \\\n",
       "0                    {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "1                    {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2                    {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "3                    {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "4                    {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "...                                                                ...   \n",
       "1528887200066129920                                                  0   \n",
       "1528888184850878464                                                594   \n",
       "1528888106589765633                                                  0   \n",
       "1528888020786479104                                                  2   \n",
       "1528887936325779456                                                  0   \n",
       "\n",
       "                     retweet favorite type  \n",
       "0                        131      712  joy  \n",
       "1                          0        0  joy  \n",
       "2                        123        0  joy  \n",
       "3                        106        0  joy  \n",
       "4                         78        0  joy  \n",
       "...                      ...      ...  ...  \n",
       "1528887200066129920        1      joy  NaN  \n",
       "1528888184850878464        0      joy  NaN  \n",
       "1528888106589765633        3      joy  NaN  \n",
       "1528888020786479104        0      joy  NaN  \n",
       "1528887936325779456        3      joy  NaN  \n",
       "\n",
       "[184725 rows x 8 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('tweets_zh.csv',converters={'id':str})\n",
    "df = df.drop_duplicates()\n",
    "# df = df[df['id'] != 'id']\n",
    "lastid = ''\n",
    "df.query('type == \"joy\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###\n",
    "# 2-fear\n",
    "keywords = ' OR '.join(fear)\n",
    "keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_tweets = maxquerry[3]\n",
    "count = int(max_tweets/7)\n",
    "print('For this stream of emoji, we collect {} Tweets'.format(max_tweets))\n",
    "print('For each day in the past week, we collect {} Tweets'.format(count))\n",
    "lastid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "import csv\n",
    "t = 'fear'\n",
    "\n",
    "print ('Twritter api:', api)\n",
    "# at least contains one relevant emoji \n",
    "#filting retweets and tweets with media\n",
    "query = keywords +'-filter:retweets'+'-filter:media' \n",
    "print ('Search for Tweets containing at least one of the following emoji', query)\n",
    "\n",
    "csvFile = open('tweets_zh.csv', 'a')\n",
    "csvWriter = csv.writer(csvFile)\n",
    "header = ['id','time', 'tweets','source','entities','retweet','favorite','type']\n",
    "\n",
    "if lastid == '':\n",
    "#     csvWriter.writerow(header) #write header to csv\n",
    "    try: \n",
    "        search = tweepy.Cursor(api.search_tweets, \n",
    "                          q=query,\n",
    "                          count=count,\n",
    "                          result_type='recent',#Include both popular and real time results in the response.\n",
    "                          lang='zh',                               \n",
    "                          until=date).items()\n",
    "        for status in search:\n",
    "            lastid = status.id\n",
    "            if len(status.text)>1:\n",
    "                csvWriter.writerow([status.id, status.created_at, status.text, \n",
    "                                    status.source, status.entities, status.retweet_count, \n",
    "                                    status.favorite_count, t])#save tweets to csv\n",
    "    except:\n",
    "        print('Something wrong!')\n",
    "        print('The last Tweet:', status.created_at, status.text)\n",
    "    print('The last id:', lastid)\n",
    "\n",
    "else:\n",
    "    try: \n",
    "        search = tweepy.Cursor(api.search_tweets, \n",
    "                               q=query,\n",
    "                              count=count,\n",
    "                              result_type='recent',\n",
    "                              lang='zh',\n",
    "                               until=date,\n",
    "                              max_id=lastid).items()\n",
    "        # the bigger the id is, the older the Tweet is. \n",
    "        # max_id setting means the scraped Tweets is older than the the oldest one we have so far\n",
    "        for status in search:\n",
    "            lastid = status.id\n",
    "            if len(status.text)>1:\n",
    "                csvWriter.writerow([status.id, status.created_at, status.text, \n",
    "                                    status.source, status.entities, status.retweet_count, \n",
    "                                    status.favorite_count, t])#save tweets to csv\n",
    "    except:\n",
    "        print('Something wrong!')\n",
    "        print('The last Tweet:', status.created_at, status.text)\n",
    "    print('The last id:', lastid)\n",
    "\n",
    "csvFile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = pd.read_csv('tweets_zh.csv',converters={'id':str})\n",
    "df = df[df['id'] != 'id']\n",
    "df = df.drop_duplicates()\n",
    "lastid = ''\n",
    "\n",
    "df.query('type == \"fear\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###\n",
    "# 3-sad\n",
    "keywords = ' OR '.join(sad)\n",
    "keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_tweets = maxquerry[4]\n",
    "count = int(max_tweets/7)*10\n",
    "print('For this stream of emoji, we collect {} Tweets'.format(max_tweets))\n",
    "print('For each day in the past week, we collect {} Tweets'.format(count))\n",
    "lastid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "import csv\n",
    "t = 'sad'\n",
    "\n",
    "print ('Twritter api:', api)\n",
    "# at least contains one relevant emoji \n",
    "#filting retweets and tweets with media\n",
    "query = keywords +'-filter:retweets'+'-filter:media' \n",
    "print ('Search for Tweets containing at least one of the following emoji', query)\n",
    "\n",
    "csvFile = open('tweets_zh.csv', 'a')\n",
    "csvWriter = csv.writer(csvFile)\n",
    "header = ['id','time', 'tweets','source','entities','retweet','favorite','type']\n",
    "\n",
    "if lastid == '':\n",
    "#     csvWriter.writerow(header) #write header to csv\n",
    "    try: \n",
    "        search = tweepy.Cursor(api.search_tweets, \n",
    "                          q=query,\n",
    "                          count=count,\n",
    "                          result_type='recent',#Include both popular and real time results in the response.\n",
    "                          lang='zh',                               \n",
    "                          until=date).items()\n",
    "        for status in search:\n",
    "            lastid = status.id\n",
    "            if len(status.text)>1:\n",
    "                csvWriter.writerow([status.id, status.created_at, status.text, \n",
    "                                    status.source, status.entities, status.retweet_count, \n",
    "                                    status.favorite_count, t])#save tweets to csv\n",
    "    except:\n",
    "        print('Something wrong!')\n",
    "        print('The last Tweet:', status.created_at, status.text)\n",
    "    print('The last id:', lastid)\n",
    "\n",
    "else:\n",
    "    try: \n",
    "        search = tweepy.Cursor(api.search_tweets, \n",
    "                               q=query,\n",
    "                              count=count,\n",
    "                              result_type='recent',\n",
    "                              lang='zh',\n",
    "                               until=date,\n",
    "                              max_id=lastid).items()\n",
    "        # the bigger the id is, the older the Tweet is. \n",
    "        # max_id setting means the scraped Tweets is older than the the oldest one we have so far\n",
    "        for status in search:\n",
    "            lastid = status.id\n",
    "            if len(status.text)>1:\n",
    "                csvWriter.writerow([status.id, status.created_at, status.text, \n",
    "                                    status.source, status.entities, status.retweet_count, \n",
    "                                    status.favorite_count, t])#save tweets to csv\n",
    "    except:\n",
    "        print('Something wrong!')\n",
    "        print('The last Tweet:', status.created_at, status.text)\n",
    "    print('The last id:', lastid)\n",
    "\n",
    "csvFile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = pd.read_csv('tweets_zh.csv',converters={'id':str})\n",
    "df = df[df['id'] != 'id']\n",
    "df = df.drop_duplicates()\n",
    "lastid = ''\n",
    "\n",
    "df.query('type == \"sad\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###\n",
    "# 4-anger\n",
    "keywords = ' OR '.join(anger)\n",
    "keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_tweets = maxquerry[1]\n",
    "count = int(max_tweets/7)\n",
    "print('For this stream of emoji, we collect {} Tweets'.format(max_tweets))\n",
    "print('For each day in the past week, we collect {} Tweets'.format(count))\n",
    "lastid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "import csv\n",
    "t = 'anger'\n",
    "\n",
    "print ('Twritter api:', api)\n",
    "# at least contains one relevant emoji \n",
    "#filting retweets and tweets with media\n",
    "query = keywords +'-filter:retweets'+'-filter:media' \n",
    "print ('Search for Tweets containing at least one of the following emoji', query)\n",
    "\n",
    "csvFile = open('tweets_zh.csv', 'a')\n",
    "csvWriter = csv.writer(csvFile)\n",
    "header = ['id','time', 'tweets','source','entities','retweet','favorite','type']\n",
    "\n",
    "if lastid == '':\n",
    "#     csvWriter.writerow(header) #write header to csv\n",
    "    try: \n",
    "        search = tweepy.Cursor(api.search_tweets, \n",
    "                          q=query,\n",
    "                          count=count,\n",
    "                          result_type='recent',#Include both popular and real time results in the response.\n",
    "                          lang='zh',                               \n",
    "                          until=date).items()\n",
    "        for status in search:\n",
    "            lastid = status.id\n",
    "            if len(status.text)>1:\n",
    "                csvWriter.writerow([status.id, status.created_at, status.text, \n",
    "                                    status.source, status.entities, status.retweet_count, \n",
    "                                    status.favorite_count, t])#save tweets to csv\n",
    "    except:\n",
    "        print('Something wrong!')\n",
    "        print('The last Tweet:', status.created_at, status.text)\n",
    "    print('The last id:', lastid)\n",
    "\n",
    "else:\n",
    "    try: \n",
    "        search = tweepy.Cursor(api.search_tweets, \n",
    "                               q=query,\n",
    "                              count=count,\n",
    "                              result_type='recent',\n",
    "                              lang='zh',\n",
    "                               until=date,\n",
    "                              max_id=lastid).items()\n",
    "        # the bigger the id is, the older the Tweet is. \n",
    "        # max_id setting means the scraped Tweets is older than the the oldest one we have so far\n",
    "        for status in search:\n",
    "            lastid = status.id\n",
    "            if len(status.text)>1:\n",
    "                csvWriter.writerow([status.id, status.created_at, status.text, \n",
    "                                    status.source, status.entities, status.retweet_count, \n",
    "                                    status.favorite_count, t])#save tweets to csv\n",
    "    except:\n",
    "        print('Something wrong!')\n",
    "        print('The last Tweet:', status.created_at, status.text)\n",
    "    print('The last id:', lastid)\n",
    "\n",
    "csvFile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = pd.read_csv('tweets_zh.csv',converters={'id':str})\n",
    "df = df[df['id'] != 'id']\n",
    "df = df.drop_duplicates()\n",
    "lastid = ''\n",
    "\n",
    "df.query('type == \"anger\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###\n",
    "# 5-surprise\n",
    "keywords = ' OR '.join(surp)\n",
    "keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_tweets = maxquerry[5]\n",
    "count = int(max_tweets/7)*100\n",
    "print('For this stream of emoji, we collect {} Tweets'.format(max_tweets))\n",
    "print('For each day in the past week, we collect {} Tweets'.format(count))\n",
    "lastid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "import csv\n",
    "t = 'surprise'\n",
    "\n",
    "print ('Twritter api:', api)\n",
    "# at least contains one relevant emoji \n",
    "#filting retweets and tweets with media\n",
    "query = keywords +'-filter:retweets'+'-filter:media' \n",
    "print ('Search for Tweets containing at least one of the following emoji', query)\n",
    "\n",
    "csvFile = open('tweets_zh.csv', 'a')\n",
    "csvWriter = csv.writer(csvFile)\n",
    "header = ['id','time', 'tweets','source','entities','retweet','favorite','type']\n",
    "\n",
    "if lastid == '':\n",
    "#     csvWriter.writerow(header) #write header to csv\n",
    "    try: \n",
    "        search = tweepy.Cursor(api.search_tweets, \n",
    "                          q=query,\n",
    "                          count=count,\n",
    "                          result_type='recent',#Include both popular and real time results in the response.\n",
    "                          lang='zh',                               \n",
    "                          until=date).items()\n",
    "        for status in search:\n",
    "            lastid = status.id\n",
    "            if len(status.text)>1:\n",
    "                csvWriter.writerow([status.id, status.created_at, status.text, \n",
    "                                    status.source, status.entities, status.retweet_count, \n",
    "                                    status.favorite_count, t])#save tweets to csv\n",
    "    except:\n",
    "        print('Something wrong!')\n",
    "        print('The last Tweet:', status.created_at, status.text)\n",
    "    print('The last id:', lastid)\n",
    "\n",
    "else:\n",
    "    try: \n",
    "        search = tweepy.Cursor(api.search_tweets, \n",
    "                               q=query,\n",
    "                              count=count,\n",
    "                              result_type='recent',\n",
    "                              lang='zh',\n",
    "                               until=date,\n",
    "                              max_id=lastid).items()\n",
    "        # the bigger the id is, the older the Tweet is. \n",
    "        # max_id setting means the scraped Tweets is older than the the oldest one we have so far\n",
    "        for status in search:\n",
    "            lastid = status.id\n",
    "            if len(status.text)>1:\n",
    "                csvWriter.writerow([status.id, status.created_at, status.text, \n",
    "                                    status.source, status.entities, status.retweet_count, \n",
    "                                    status.favorite_count, t])#save tweets to csv\n",
    "    except:\n",
    "        print('Something wrong!')\n",
    "        print('The last Tweet:', status.created_at, status.text)\n",
    "    print('The last id:', lastid)\n",
    "\n",
    "csvFile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = pd.read_csv('tweets_zh.csv',converters={'id':str})\n",
    "df = df[df['id'] != 'id']\n",
    "df = df.drop_duplicates()\n",
    "lastid = ''\n",
    "\n",
    "df.query('type == \"surprise\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###\n",
    "# 5-disgust\n",
    "keywords = ' OR '.join(disgust)\n",
    "keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_tweets = maxquerry[2]\n",
    "count = int(max_tweets/7)\n",
    "print('For this stream of emoji, we collect {} Tweets'.format(max_tweets))\n",
    "print('For each day in the past week, we collect {} Tweets'.format(count))\n",
    "lastid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "import csv\n",
    "t = 'disgust'\n",
    "\n",
    "print ('Twritter api:', api)\n",
    "# at least contains one relevant emoji \n",
    "#filting retweets and tweets with media\n",
    "query = keywords +'-filter:retweets'+'-filter:media' \n",
    "print ('Search for Tweets containing at least one of the following emoji', query)\n",
    "\n",
    "csvFile = open('tweets_zh.csv', 'a')\n",
    "csvWriter = csv.writer(csvFile)\n",
    "header = ['Unnamed: 0\t','id','time', 'tweets','source','entities','retweet','favorite','type']\n",
    "\n",
    "if lastid == '':\n",
    "    csvWriter.writerow(header) #write header to csv\n",
    "    try: \n",
    "        search = tweepy.Cursor(api.search_tweets, \n",
    "                          q=query,\n",
    "                          count=count,\n",
    "                          result_type='recent',#Include both popular and real time results in the response.\n",
    "                          lang='zh',                               \n",
    "                          until=date).items()\n",
    "        for status in search:\n",
    "            lastid = status.id\n",
    "            if len(status.text)>1:\n",
    "                csvWriter.writerow(['',status.id, status.created_at, status.text, \n",
    "                                    status.source, status.entities, status.retweet_count, \n",
    "                                    status.favorite_count, t])#save tweets to csv\n",
    "    except:\n",
    "        print('Something wrong!')\n",
    "        print('The last Tweet:', status.created_at, status.text)\n",
    "    print('The last id:', lastid)\n",
    "\n",
    "else:\n",
    "    try: \n",
    "        search = tweepy.Cursor(api.search_tweets, \n",
    "                               q=query,\n",
    "                              count=count,\n",
    "                              result_type='recent',\n",
    "                              lang='zh',\n",
    "                               until=date,\n",
    "                              max_id=lastid).items()\n",
    "        # the bigger the id is, the older the Tweet is. \n",
    "        # max_id setting means the scraped Tweets is older than the the oldest one we have so far\n",
    "        for status in search:\n",
    "            lastid = status.id\n",
    "            if len(status.text)>1:\n",
    "                csvWriter.writerow([status.id, status.created_at, status.text, \n",
    "                                    status.source, status.entities, status.retweet_count, \n",
    "                                    status.favorite_count, t])#save tweets to csv\n",
    "    except:\n",
    "        print('Something wrong!')\n",
    "        print('The last Tweet:', status.created_at, status.text)\n",
    "    print('The last id:', lastid)\n",
    "\n",
    "csvFile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-c846513d6f76>:2: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('tweets_zh.csv',converters={'id':str})\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>time</th>\n",
       "      <th>tweets</th>\n",
       "      <th>source</th>\n",
       "      <th>entities</th>\n",
       "      <th>retweet</th>\n",
       "      <th>favorite</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41081</th>\n",
       "      <td>41081</td>\n",
       "      <td>1516204462452654080</td>\n",
       "      <td>2022-04-18 23:58:08+00:00</td>\n",
       "      <td>RT @songyuelu: 中共国现在之所以这样\\n\\n就是因为这一代吃屎长大的人正在当权...</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>disgust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41082</th>\n",
       "      <td>41082</td>\n",
       "      <td>1516203804122779653</td>\n",
       "      <td>2022-04-18 23:55:31+00:00</td>\n",
       "      <td>@qptW4DfCPRrOMRK @NanTianFeiYun @ShouYi18 蛙蛙😁爬...</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>disgust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41083</th>\n",
       "      <td>41083</td>\n",
       "      <td>1516202297722048513</td>\n",
       "      <td>2022-04-18 23:49:32+00:00</td>\n",
       "      <td>RT @Rubbish15074778: 開sem快樂💩最近又沉迷睇人自己打飛機射精🤤喺咁射...</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>{'hashtags': [{'text': '淫', 'indices': [59, 61...</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>disgust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41084</th>\n",
       "      <td>41084</td>\n",
       "      <td>1516202211948494850</td>\n",
       "      <td>2022-04-18 23:49:11+00:00</td>\n",
       "      <td>RT @HDX1069: 📷原创直男拉屎系列（勾引）回归第一期之 勾引21岁武警兵哥哥👅💦✈...</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>{'hashtags': [{'text': '直男重口', 'indices': [88,...</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>disgust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41085</th>\n",
       "      <td>41085</td>\n",
       "      <td>1516202019605782530</td>\n",
       "      <td>2022-04-18 23:48:25+00:00</td>\n",
       "      <td>我今儿一早是被臭醒的，因为奶茶憋不住💩，在房间角落里拉了一泡新鲜臭烘烘的💩，还在那儿刨地板故...</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>disgust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184511</th>\n",
       "      <td>184511</td>\n",
       "      <td>1530070554845192192</td>\n",
       "      <td>2022-05-27 06:17:02+00:00</td>\n",
       "      <td>RT @QQzk_prs: 在1986年，被稱為正常的夏天。\\n在今天，他們將地圖塗成橙色，...</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>disgust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184512</th>\n",
       "      <td>184512</td>\n",
       "      <td>1530068008110014464</td>\n",
       "      <td>2022-05-27 06:06:55+00:00</td>\n",
       "      <td>RT @QQzk_prs: 在1986年，被稱為正常的夏天。\\n在今天，他們將地圖塗成橙色，...</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>disgust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184513</th>\n",
       "      <td>184513</td>\n",
       "      <td>1530066113043865602</td>\n",
       "      <td>2022-05-27 05:59:23+00:00</td>\n",
       "      <td>@nytchinese 美国这真是公开耍流氓，公开不要脸啊。自己下三滥，你们就看不得别人好💩</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>disgust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184514</th>\n",
       "      <td>184514</td>\n",
       "      <td>1530066091497889797</td>\n",
       "      <td>2022-05-27 05:59:18+00:00</td>\n",
       "      <td>RT @QQzk_prs: 在1986年，被稱為正常的夏天。\\n在今天，他們將地圖塗成橙色，...</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>disgust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184515</th>\n",
       "      <td>184515</td>\n",
       "      <td>1530062804019220480</td>\n",
       "      <td>2022-05-27 05:46:14+00:00</td>\n",
       "      <td>@xiaolibujiangl2 你可真是忠实的共匪狗💩💩💩真正该死的是你这种舔共的辣鸡和你...</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>disgust</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5363 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0                   id                       time  \\\n",
       "41081        41081  1516204462452654080  2022-04-18 23:58:08+00:00   \n",
       "41082        41082  1516203804122779653  2022-04-18 23:55:31+00:00   \n",
       "41083        41083  1516202297722048513  2022-04-18 23:49:32+00:00   \n",
       "41084        41084  1516202211948494850  2022-04-18 23:49:11+00:00   \n",
       "41085        41085  1516202019605782530  2022-04-18 23:48:25+00:00   \n",
       "...            ...                  ...                        ...   \n",
       "184511      184511  1530070554845192192  2022-05-27 06:17:02+00:00   \n",
       "184512      184512  1530068008110014464  2022-05-27 06:06:55+00:00   \n",
       "184513      184513  1530066113043865602  2022-05-27 05:59:23+00:00   \n",
       "184514      184514  1530066091497889797  2022-05-27 05:59:18+00:00   \n",
       "184515      184515  1530062804019220480  2022-05-27 05:46:14+00:00   \n",
       "\n",
       "                                                   tweets  \\\n",
       "41081   RT @songyuelu: 中共国现在之所以这样\\n\\n就是因为这一代吃屎长大的人正在当权...   \n",
       "41082   @qptW4DfCPRrOMRK @NanTianFeiYun @ShouYi18 蛙蛙😁爬...   \n",
       "41083   RT @Rubbish15074778: 開sem快樂💩最近又沉迷睇人自己打飛機射精🤤喺咁射...   \n",
       "41084   RT @HDX1069: 📷原创直男拉屎系列（勾引）回归第一期之 勾引21岁武警兵哥哥👅💦✈...   \n",
       "41085   我今儿一早是被臭醒的，因为奶茶憋不住💩，在房间角落里拉了一泡新鲜臭烘烘的💩，还在那儿刨地板故...   \n",
       "...                                                   ...   \n",
       "184511  RT @QQzk_prs: 在1986年，被稱為正常的夏天。\\n在今天，他們將地圖塗成橙色，...   \n",
       "184512  RT @QQzk_prs: 在1986年，被稱為正常的夏天。\\n在今天，他們將地圖塗成橙色，...   \n",
       "184513     @nytchinese 美国这真是公开耍流氓，公开不要脸啊。自己下三滥，你们就看不得别人好💩   \n",
       "184514  RT @QQzk_prs: 在1986年，被稱為正常的夏天。\\n在今天，他們將地圖塗成橙色，...   \n",
       "184515  @xiaolibujiangl2 你可真是忠实的共匪狗💩💩💩真正该死的是你这种舔共的辣鸡和你...   \n",
       "\n",
       "                     source  \\\n",
       "41081       Twitter Web App   \n",
       "41082       Twitter Web App   \n",
       "41083   Twitter for Android   \n",
       "41084       Twitter Web App   \n",
       "41085    Twitter for iPhone   \n",
       "...                     ...   \n",
       "184511   Twitter for iPhone   \n",
       "184512      Twitter Web App   \n",
       "184513      Twitter Web App   \n",
       "184514   Twitter for iPhone   \n",
       "184515   Twitter for iPhone   \n",
       "\n",
       "                                                 entities  retweet favorite  \\\n",
       "41081   {'hashtags': [], 'symbols': [], 'user_mentions...       54        0   \n",
       "41082   {'hashtags': [], 'symbols': [], 'user_mentions...        0        0   \n",
       "41083   {'hashtags': [{'text': '淫', 'indices': [59, 61...       61        0   \n",
       "41084   {'hashtags': [{'text': '直男重口', 'indices': [88,...      109        0   \n",
       "41085   {'hashtags': [], 'symbols': [], 'user_mentions...        0       41   \n",
       "...                                                   ...      ...      ...   \n",
       "184511  {'hashtags': [], 'symbols': [], 'user_mentions...       79        0   \n",
       "184512  {'hashtags': [], 'symbols': [], 'user_mentions...       79        0   \n",
       "184513  {'hashtags': [], 'symbols': [], 'user_mentions...        0        9   \n",
       "184514  {'hashtags': [], 'symbols': [], 'user_mentions...       79        0   \n",
       "184515  {'hashtags': [], 'symbols': [], 'user_mentions...        0        7   \n",
       "\n",
       "           type  \n",
       "41081   disgust  \n",
       "41082   disgust  \n",
       "41083   disgust  \n",
       "41084   disgust  \n",
       "41085   disgust  \n",
       "...         ...  \n",
       "184511  disgust  \n",
       "184512  disgust  \n",
       "184513  disgust  \n",
       "184514  disgust  \n",
       "184515  disgust  \n",
       "\n",
       "[5363 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('tweets_zh.csv',converters={'id':str})\n",
    "df = df.drop_duplicates()\n",
    "lastid = ''\n",
    "\n",
    "df.query('type == \"disgust\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>time</th>\n",
       "      <th>tweets</th>\n",
       "      <th>source</th>\n",
       "      <th>entities</th>\n",
       "      <th>retweet</th>\n",
       "      <th>favorite</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1515279424953876481</td>\n",
       "      <td>2022-04-16 10:42:22+00:00</td>\n",
       "      <td>BOY STORY 默契大挑战 EP.02\\n\\n▶️https://t.co/G5T5Mc...</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>131</td>\n",
       "      <td>712</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1516204930461159425</td>\n",
       "      <td>2022-04-18 23:59:59+00:00</td>\n",
       "      <td>@QFsFP3yAZKKFBwK 节省网络存储空间？😂</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>joy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1516204918977470467</td>\n",
       "      <td>2022-04-18 23:59:57+00:00</td>\n",
       "      <td>RT @Baoliaogeming64: 一部手机拯救了乌克兰战士的生命——一颗 7,62 ...</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>joy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1516204916217311232</td>\n",
       "      <td>2022-04-18 23:59:56+00:00</td>\n",
       "      <td>RT @S7i5FV0JOz6sV3A: 去tmd病毒！泰国泼水节好开心😃 https://...</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>joy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1516204913818501120</td>\n",
       "      <td>2022-04-18 23:59:55+00:00</td>\n",
       "      <td>RT @Fang961103: 沒人就脫一下\\n影片有人想看嗎😊 https://t.co/...</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>joy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184577</th>\n",
       "      <td>1530318420805296128</td>\n",
       "      <td>2022-05-27 22:41:58+00:00</td>\n",
       "      <td>RT @soraisuu: #FulGallery 一些簡單的😳 https://t.co/...</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>{'hashtags': [{'text': 'FulGallery', 'indices'...</td>\n",
       "      <td>222</td>\n",
       "      <td>0</td>\n",
       "      <td>surprise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184578</th>\n",
       "      <td>1530317406920400896</td>\n",
       "      <td>2022-05-27 22:37:56+00:00</td>\n",
       "      <td>@RoyalvESPNDE @KUI__YANG 都不知道反抗是为了什么？\\n也不知道什么是...</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>surprise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184579</th>\n",
       "      <td>1530316150244098049</td>\n",
       "      <td>2022-05-27 22:32:56+00:00</td>\n",
       "      <td>@__Inty__ 竟然没有德州😲</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>surprise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184580</th>\n",
       "      <td>1530316061261713408</td>\n",
       "      <td>2022-05-27 22:32:35+00:00</td>\n",
       "      <td>RT @soraisuu: #FulGallery 一些簡單的😳 https://t.co/...</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>{'hashtags': [{'text': 'FulGallery', 'indices'...</td>\n",
       "      <td>222</td>\n",
       "      <td>0</td>\n",
       "      <td>surprise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184581</th>\n",
       "      <td>1530313827405164544</td>\n",
       "      <td>2022-05-27 22:23:42+00:00</td>\n",
       "      <td>RT @wanderlust_49: 💬：喜歡跟留真還是彩領當室友？\\n🐺：有時蠻想念睡在雙...</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>surprise</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184582 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id                       time  \\\n",
       "0       1515279424953876481  2022-04-16 10:42:22+00:00   \n",
       "1       1516204930461159425  2022-04-18 23:59:59+00:00   \n",
       "2       1516204918977470467  2022-04-18 23:59:57+00:00   \n",
       "3       1516204916217311232  2022-04-18 23:59:56+00:00   \n",
       "4       1516204913818501120  2022-04-18 23:59:55+00:00   \n",
       "...                     ...                        ...   \n",
       "184577  1530318420805296128  2022-05-27 22:41:58+00:00   \n",
       "184578  1530317406920400896  2022-05-27 22:37:56+00:00   \n",
       "184579  1530316150244098049  2022-05-27 22:32:56+00:00   \n",
       "184580  1530316061261713408  2022-05-27 22:32:35+00:00   \n",
       "184581  1530313827405164544  2022-05-27 22:23:42+00:00   \n",
       "\n",
       "                                                   tweets  \\\n",
       "0       BOY STORY 默契大挑战 EP.02\\n\\n▶️https://t.co/G5T5Mc...   \n",
       "1                             @QFsFP3yAZKKFBwK 节省网络存储空间？😂   \n",
       "2       RT @Baoliaogeming64: 一部手机拯救了乌克兰战士的生命——一颗 7,62 ...   \n",
       "3       RT @S7i5FV0JOz6sV3A: 去tmd病毒！泰国泼水节好开心😃 https://...   \n",
       "4       RT @Fang961103: 沒人就脫一下\\n影片有人想看嗎😊 https://t.co/...   \n",
       "...                                                   ...   \n",
       "184577  RT @soraisuu: #FulGallery 一些簡單的😳 https://t.co/...   \n",
       "184578  @RoyalvESPNDE @KUI__YANG 都不知道反抗是为了什么？\\n也不知道什么是...   \n",
       "184579                                  @__Inty__ 竟然没有德州😲   \n",
       "184580  RT @soraisuu: #FulGallery 一些簡單的😳 https://t.co/...   \n",
       "184581  RT @wanderlust_49: 💬：喜歡跟留真還是彩領當室友？\\n🐺：有時蠻想念睡在雙...   \n",
       "\n",
       "                     source  \\\n",
       "0        Twitter for iPhone   \n",
       "1       Twitter for Android   \n",
       "2           Twitter Web App   \n",
       "3       Twitter for Android   \n",
       "4       Twitter for Android   \n",
       "...                     ...   \n",
       "184577      Twitter Web App   \n",
       "184578  Twitter for Android   \n",
       "184579  Twitter for Android   \n",
       "184580   Twitter for iPhone   \n",
       "184581  Twitter for Android   \n",
       "\n",
       "                                                 entities  retweet  favorite  \\\n",
       "0       {'hashtags': [], 'symbols': [], 'user_mentions...      131       712   \n",
       "1       {'hashtags': [], 'symbols': [], 'user_mentions...        0         0   \n",
       "2       {'hashtags': [], 'symbols': [], 'user_mentions...      123         0   \n",
       "3       {'hashtags': [], 'symbols': [], 'user_mentions...      106         0   \n",
       "4       {'hashtags': [], 'symbols': [], 'user_mentions...       78         0   \n",
       "...                                                   ...      ...       ...   \n",
       "184577  {'hashtags': [{'text': 'FulGallery', 'indices'...      222         0   \n",
       "184578  {'hashtags': [], 'symbols': [], 'user_mentions...        0         5   \n",
       "184579  {'hashtags': [], 'symbols': [], 'user_mentions...        0         5   \n",
       "184580  {'hashtags': [{'text': 'FulGallery', 'indices'...      222         0   \n",
       "184581  {'hashtags': [], 'symbols': [], 'user_mentions...       44         0   \n",
       "\n",
       "            type  \n",
       "0            joy  \n",
       "1            joy  \n",
       "2            joy  \n",
       "3            joy  \n",
       "4            joy  \n",
       "...          ...  \n",
       "184577  surprise  \n",
       "184578  surprise  \n",
       "184579  surprise  \n",
       "184580  surprise  \n",
       "184581  surprise  \n",
       "\n",
       "[184582 rows x 8 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "######ignore#########"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df.reset_index(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_csv('tweets_zh_xx.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df1 = df[df['type'].notnull()]\n",
    "# # df1 = df1.iloc[:,2:]\n",
    "# df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2 = df[df['type'].isnull()]\n",
    "# # df2 = df2.iloc[:,:8]\n",
    "# # df2.columns = ['id','time','tweets','source','entities','retweet','favorite','type']\n",
    "# df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df1.append(df2, ignore_index=True)\n",
    "# df.to_csv('tweets_zh_xx.csv')\n",
    "# df"
   ]
  }
 ],
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