04_Pattern_Analyzer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ef07c327-c5ee-420e-b85f-390c92ba1de0",
   "metadata": {},
   "source": [
    "## Combine everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "900b41cf-cd31-42ec-b675-76ae6d8bf09c",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Read data\n",
    "import pandas as pd\n",
    "import re\n",
    "from collections import Counter\n",
    "import nltk\n",
    "from nltk.tokenize import  word_tokenize\n",
    "from pandarallel import pandarallel\n",
    "import pickle\n",
    "\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2af54b62-7e66-4054-8ac7-4a4b6467cb0c",
   "metadata": {},
   "source": [
    "### Actual dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0a783e7b-6bea-465d-a484-427bcb6b02e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/IPython/core/magics/execution.py:1335: DtypeWarning: Columns (6) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code, glob, local_ns)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.4 s, sys: 1.85 s, total: 15.3 s\n",
      "Wall time: 19.5 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2218243, 14)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "post_data_full = pd.read_csv('wallstreetbets_submissions_zstextract.csv')\n",
    "post_data_full.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a48e2fc-8e19-4e85-a510-c587c3abf065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-04-11 16:40:40')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "post_data_full['timestamp'] = post_data_full['created_utc'].apply(datetime.datetime.fromtimestamp)\n",
    "post_data_full['timestamp'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "660fce0f-f351-4da5-be63-a59946f845d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    # Remove all text between square brackets\n",
    "    text = re.sub(r'\\[.*?\\]', '', text)\n",
    "    \n",
    "    # Remove all text between parentheses\n",
    "    text = re.sub(r'\\(.*?\\)', '', text)\n",
    "    \n",
    "    # Remove all text between curly braces\n",
    "    text = re.sub(r'\\{.*?\\}', '', text)\n",
    "    \n",
    "    # Remove all text between angle brackets\n",
    "    text = re.sub(r'<.*?>', '', text)\n",
    "    \n",
    "    # Remove all text between double quotes\n",
    "    #text = re.sub(r'\"(.*?)\"', '', text)\n",
    "    \n",
    "    # Remove all text between single quotes\n",
    "    #text = re.sub(r\"'(.*?)'\", '', text)\n",
    "    \n",
    "    # Remove everything between **____**\n",
    "    text = re.sub(r'\\*\\*(.*?)\\*\\*','',text)\n",
    "    \n",
    "    # Remove words followed by ^\n",
    "    text = re.sub(r'(\\^\\w*)','',text)\n",
    "    \n",
    "    # Remove mentions, hashtags, links\n",
    "    text = re.sub(r'\\||:|\\-|\\n|(@\\w+.*?)|(http\\w\\S+.*?)|(#\\w+)','',text)\n",
    "    \n",
    "    # Remove all ^Your daily posts\n",
    "    text = re.sub(r'^Your daily trading discussion.*|^We are now using AutoModerator.*|^Every time a new submission.*|^Trading discussion only.*','',text)\n",
    "    \n",
    "    # Remove all newlines, tabs, and extra whitespace\n",
    "    text = re.sub(r'[\\n\\t]+', ' ', text)\n",
    "    text = re.sub(r'\\s{2,}', ' ', text)\n",
    "    \n",
    "    \n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9185a600-15a1-420c-be32-da17e1ee5f18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 8 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n",
      "CPU times: user 2.56 s, sys: 1 s, total: 3.56 s\n",
      "Wall time: 10 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "clean_text\n",
       "            1400263\n",
       "nan          661710\n",
       "&amp;;         1012\n",
       "                648\n",
       "Discuss.        509\n",
       "Name: author, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "pandarallel.initialize()\n",
    "post_data_full['clean_text'] = post_data_full['body'].parallel_apply(lambda x: clean_text(str(x)))\n",
    "post_data_full = post_data_full[['id','title','clean_text','author','timestamp','upvotes','num_comments','score']]\n",
    "post_data_full.groupby(['clean_text'])['author'].count().sort_values(ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "73fb6ff4-2b2e-4ec1-a11a-2108f6a76bfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Creating a copy\n",
    "df = post_data_full.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d46565-352b-49c6-b310-055f6b3028cb",
   "metadata": {},
   "source": [
    "### Topic LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9d2bd859-568b-46ee-a50b-b1ba59f04a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'title', 'clean_text', 'author', 'timestamp', 'upvotes',\n",
       "       'num_comments', 'score', 'topic_label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label = pd.read_parquet('label_lda.parquet')\n",
    "\n",
    "df['topic_label'] = label\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "899ea86b-2acb-4efa-8388-78b6980bc93a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#df['date'] = pd.to_datetime(df['date'])\n",
    "df['time_period'] = pd.PeriodIndex(df['timestamp'], freq='Q')\n",
    "count_df = df.groupby(['time_period', 'topic_label']).size().reset_index(name='count')\n",
    "\n",
    "count_df['time_period'] = count_df['time_period'].dt.strftime('%Y-Q%q')\n",
    "\n",
    "# Plot the topic timeline\n",
    "fig, ax = plt.subplots()\n",
    "for topic in count_df.loc[count_df['topic_label'] != 0, 'topic_label'].unique():\n",
    "    topic_data = count_df[count_df['topic_label'] == topic]\n",
    "    ax.scatter(topic_data['time_period'], topic_data['count'], label='Topic {}'.format(topic))\n",
    "ax.set_xlabel('Time Period')\n",
    "ax.set_ylabel('Number of Documents')\n",
    "ax.legend()\n",
    "plt.xticks(rotation=90)\n",
    "#ax.set_xticks(quarter_periods)\n",
    "#ax.set_xticklabels(quarter_periods.strftime('%Y-Q%q'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd181806-e613-4489-84db-2308820de80a",
   "metadata": {},
   "source": [
    "Topic 0: Robinhood app, stock trading, and related terminology like \"dd\" (due diligence), \"rh\" (Robinhood), and \"stonks\".\n",
    "\n",
    "Topic 1: Options trading, including call and put options, and various timeframes (day, week, year).\n",
    "\n",
    "Topic 2: General discussions on stocks and the market, with terms like \"bb\" (Blackberry), \"wall\" (Wall Street), and \"street\".\n",
    "\n",
    "Topic 3: Short selling, stock prices, and related terms like \"squeeze\" (short squeeze), \"tendies\" (profits), and \"nio\" (a Chinese electric vehicle company).\n",
    "\n",
    "Topic 4: Hedge funds, news, and various other topics like the SEC (Securities and Exchange Commission), Donald Trump, and cryptocurrencies.\n",
    "\n",
    "Topic 5: Buying, holding, and selling stocks, as well as various other related terms like \"dip\" (a temporary drop in stock prices) and \"earnings\".\n",
    "\n",
    "Topic 6: Specific stocks that have gained a lot of attention on WSB, including AMC, Nokia, and Dogecoin, as well as related terms like \"moon\" and \"rocket\".\n",
    "\n",
    "Topic 7: General discussions on stocks and related topics, as well as various other terms like \"dogecoin\" and \"autist\".\n",
    "\n",
    "Topic 8: Discussions related to investing, including the WSB community itself, as well as related terms like \"retard\" (used humorously) and \"gain\".\n",
    "\n",
    "Topic 9: GameStop (GME), a stock that gained a lot of attention on WSB in early 2021, as well as related terms like \"next\" and \"buy\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "35e1d464-7803-4c6e-9c7e-06dfd1fbd45e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'title', 'clean_text', 'author', 'timestamp', 'upvotes',\n",
       "       'num_comments', 'score', 'topic_label', 'time_period'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d3503dc-6755-4364-8040-be5ebcee6329",
   "metadata": {},
   "source": [
    "### NER Spacy - Large"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3ce0fd28-f868-4945-a5c9-39222df223a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'title', 'clean_text', 'author', 'timestamp', 'upvotes',\n",
       "       'num_comments', 'score', 'topic_label', 'title_ner_lg', 'text_ner_lg'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ner_lg = pd.read_parquet('ner_lg.parquet')\n",
    "\n",
    "df = pd.concat([df, ner_lg], axis=1)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7e86728d-5138-4afe-8acb-1567da0b687b",
   "metadata": {},
   "outputs": [
    {
     "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>title</th>\n",
       "      <th>clean_text</th>\n",
       "      <th>author</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>upvotes</th>\n",
       "      <th>num_comments</th>\n",
       "      <th>score</th>\n",
       "      <th>topic_label</th>\n",
       "      <th>title_ner_lg</th>\n",
       "      <th>text_ner_lg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s4jw1</td>\n",
       "      <td>Earnings season is here.  Place your bets.</td>\n",
       "      <td>I know that /r/investing is a great place for ...</td>\n",
       "      <td>[deleted]</td>\n",
       "      <td>2012-04-11 16:40:40</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>[Google]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s6r57</td>\n",
       "      <td>GOOG - beat estimates, price barely rises.</td>\n",
       "      <td>nan</td>\n",
       "      <td>[deleted]</td>\n",
       "      <td>2012-04-12 20:37:31</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sd5ai</td>\n",
       "      <td>My poorly timed opening position for AAPL earn...</td>\n",
       "      <td>So I missed out on GOOG, which is probably a g...</td>\n",
       "      <td>[deleted]</td>\n",
       "      <td>2012-04-16 22:29:37</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[OTM, BTO, BTO]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>se66f</td>\n",
       "      <td>Anyone betting on VVUS and their potential app...</td>\n",
       "      <td>I'm normally a long, but I've created a second...</td>\n",
       "      <td>[deleted]</td>\n",
       "      <td>2012-04-17 13:41:30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>[VVUS, Qnexa]</td>\n",
       "      <td>[Qnexa, FDA]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sinsk</td>\n",
       "      <td>After HGSI spikes 97%, will share price drop a...</td>\n",
       "      <td>nan</td>\n",
       "      <td>CheeseYogi</td>\n",
       "      <td>2012-04-19 23:21:16</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>[HGSI]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                                              title  \\\n",
       "0  s4jw1         Earnings season is here.  Place your bets.   \n",
       "1  s6r57         GOOG - beat estimates, price barely rises.   \n",
       "2  sd5ai  My poorly timed opening position for AAPL earn...   \n",
       "3  se66f  Anyone betting on VVUS and their potential app...   \n",
       "4  sinsk  After HGSI spikes 97%, will share price drop a...   \n",
       "\n",
       "                                          clean_text      author  \\\n",
       "0  I know that /r/investing is a great place for ...   [deleted]   \n",
       "1                                                nan   [deleted]   \n",
       "2  So I missed out on GOOG, which is probably a g...   [deleted]   \n",
       "3  I'm normally a long, but I've created a second...   [deleted]   \n",
       "4                                                nan  CheeseYogi   \n",
       "\n",
       "            timestamp  upvotes  num_comments  score  topic_label  \\\n",
       "0 2012-04-11 16:40:40       16            22     13            2   \n",
       "1 2012-04-12 20:37:31        5             0      2            0   \n",
       "2 2012-04-16 22:29:37       14            21     12            1   \n",
       "3 2012-04-17 13:41:30        1             0      1            2   \n",
       "4 2012-04-19 23:21:16        5             0      4            0   \n",
       "\n",
       "    title_ner_lg      text_ner_lg  \n",
       "0             []         [Google]  \n",
       "1             []               []  \n",
       "2             []  [OTM, BTO, BTO]  \n",
       "3  [VVUS, Qnexa]     [Qnexa, FDA]  \n",
       "4         [HGSI]               []  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48a37136-8533-43fc-adcf-0b1a092028da",
   "metadata": {},
   "source": [
    "### Sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0a85c612-051a-4cdc-8eb7-6bbb1e39f417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'title', 'clean_text', 'author', 'timestamp', 'upvotes',\n",
       "       'num_comments', 'score', 'topic_label', 'title_ner_lg', 'text_ner_lg',\n",
       "       'sentiment', 'sentiment_title', 'polarity', 'subjectivity',\n",
       "       'polarity_title', 'subjectivity_title'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Sentiment from Pattern Analyzer\n",
    "sent_patAna = pd.read_parquet('sentiment_title_text.parquet')\n",
    "\n",
    "df = pd.concat([df, sent_patAna], axis=1)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dcb0fbf5-0878-4883-8c1e-24142c45302a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'title', 'clean_text', 'author', 'timestamp', 'upvotes',\n",
       "       'num_comments', 'score', 'topic_label', 'title_ner_lg', 'text_ner_lg',\n",
       "       'sentiment_text', 'sentiment_title', 'polarity_text',\n",
       "       'subjectivity_text', 'polarity_title', 'subjectivity_title'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename(columns={'sentiment': 'sentiment_text', 'polarity':'polarity_text',\n",
    "                        'subjectivity':'subjectivity_text'})\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "378a3a74-e605-47d0-8df5-b824ffb9ab6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'title', 'clean_text', 'author', 'timestamp', 'upvotes',\n",
       "       'num_comments', 'score', 'topic_label', 'title_ner_lg', 'text_ner_lg',\n",
       "       'sentiment_text', 'sentiment_title', 'polarity_text',\n",
       "       'subjectivity_text', 'polarity_title', 'subjectivity_title', 'neg_text',\n",
       "       'neu_text', 'pos_text', 'compound', 'sentiment_text_vader', 'neg_title',\n",
       "       'neu_title', 'pos_title', 'compound', 'sentiment_title_vader'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Sentiment from Vader\n",
    "sent_vader_text = pd.read_parquet('sentiment_vader_text.parquet')\n",
    "sent_vader_title = pd.read_parquet('sentiment_vader_title.parquet')\n",
    "\n",
    "sent_vader_text = sent_vader_text.rename(columns={'sentiment': 'sentiment_text_vader', \n",
    "                                                  'pos':'pos_text',\n",
    "                                                  'neg':'neg_text',\n",
    "                                                  'neu':'neu_text'})\n",
    "sent_vader_title = sent_vader_title.rename(columns={'sentiment': 'sentiment_title_vader', \n",
    "                                                  'pos':'pos_title',\n",
    "                                                  'neg':'neg_title',\n",
    "                                                  'neu':'neu_title'})\n",
    "\n",
    "df = pd.concat([df, sent_vader_text, sent_vader_title], axis=1)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2c17cc1-2e8c-4e6a-9f2c-48a62bdaa916",
   "metadata": {},
   "source": [
    "## Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f6821645-ff42-4f30-bfe3-b121377c59ef",
   "metadata": {},
   "outputs": [
    {
     "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>title</th>\n",
       "      <th>clean_text</th>\n",
       "      <th>author</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>upvotes</th>\n",
       "      <th>num_comments</th>\n",
       "      <th>score</th>\n",
       "      <th>topic_label</th>\n",
       "      <th>title_ner_lg</th>\n",
       "      <th>...</th>\n",
       "      <th>neg_text</th>\n",
       "      <th>neu_text</th>\n",
       "      <th>pos_text</th>\n",
       "      <th>compound</th>\n",
       "      <th>sentiment_text_vader</th>\n",
       "      <th>neg_title</th>\n",
       "      <th>neu_title</th>\n",
       "      <th>pos_title</th>\n",
       "      <th>compound</th>\n",
       "      <th>sentiment_title_vader</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2218238</th>\n",
       "      <td>1006q7e</td>\n",
       "      <td>Gay bear</td>\n",
       "      <td></td>\n",
       "      <td>Anychefanytime</td>\n",
       "      <td>2022-12-31 23:39:35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218239</th>\n",
       "      <td>1006ur4</td>\n",
       "      <td>Please identify which one of you was sitting o...</td>\n",
       "      <td>nan</td>\n",
       "      <td>dogelonian</td>\n",
       "      <td>2022-12-31 23:46:12</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.3182</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218240</th>\n",
       "      <td>1006xbr</td>\n",
       "      <td>Society</td>\n",
       "      <td>nan</td>\n",
       "      <td>Anychefanytime</td>\n",
       "      <td>2022-12-31 23:50:08</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[Society]</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218241</th>\n",
       "      <td>1006ygl</td>\n",
       "      <td>Me at the end of 2022.</td>\n",
       "      <td>nan</td>\n",
       "      <td>the-real-ben-dover</td>\n",
       "      <td>2022-12-31 23:51:48</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218242</th>\n",
       "      <td>1006yjy</td>\n",
       "      <td>The gift I want after FTX</td>\n",
       "      <td></td>\n",
       "      <td>[deleted]</td>\n",
       "      <td>2022-12-31 23:51:57</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>[FTX]</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0.4939</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                                              title  \\\n",
       "2218238  1006q7e                                           Gay bear   \n",
       "2218239  1006ur4  Please identify which one of you was sitting o...   \n",
       "2218240  1006xbr                                            Society   \n",
       "2218241  1006ygl                             Me at the end of 2022.   \n",
       "2218242  1006yjy                          The gift I want after FTX   \n",
       "\n",
       "        clean_text              author           timestamp  upvotes  \\\n",
       "2218238                 Anychefanytime 2022-12-31 23:39:35        0   \n",
       "2218239        nan          dogelonian 2022-12-31 23:46:12        0   \n",
       "2218240        nan      Anychefanytime 2022-12-31 23:50:08        0   \n",
       "2218241        nan  the-real-ben-dover 2022-12-31 23:51:48        0   \n",
       "2218242                      [deleted] 2022-12-31 23:51:57        0   \n",
       "\n",
       "         num_comments  score  topic_label title_ner_lg  ... neg_text neu_text  \\\n",
       "2218238             1      1            0           []  ...      0.0      0.0   \n",
       "2218239            17     21            0           []  ...      0.0      1.0   \n",
       "2218240             2      1            0    [Society]  ...      0.0      1.0   \n",
       "2218241             6     21            0           []  ...      0.0      1.0   \n",
       "2218242            16     32            0        [FTX]  ...      0.0      0.0   \n",
       "\n",
       "        pos_text  compound  sentiment_text_vader  neg_title  neu_title  \\\n",
       "2218238      0.0       0.0               Neutral        0.0      1.000   \n",
       "2218239      0.0       0.0               Neutral        0.0      0.813   \n",
       "2218240      0.0       0.0               Neutral        0.0      1.000   \n",
       "2218241      0.0       0.0               Neutral        0.0      1.000   \n",
       "2218242      0.0       0.0               Neutral        0.0      0.488   \n",
       "\n",
       "         pos_title  compound  sentiment_title_vader  \n",
       "2218238      0.000    0.0000                Neutral  \n",
       "2218239      0.187    0.3182               Positive  \n",
       "2218240      0.000    0.0000                Neutral  \n",
       "2218241      0.000    0.0000                Neutral  \n",
       "2218242      0.512    0.4939               Positive  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "37ec4d15-ed7e-4c77-98e8-908d8c8fa7b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('GME', 141937),\n",
       " ('AMC', 76315),\n",
       " ('WSB', 29340),\n",
       " ('Robinhood', 25420),\n",
       " ('BB', 14812),\n",
       " ('Tesla', 12111),\n",
       " ('PLTR', 11019),\n",
       " ('MOON', 8472),\n",
       " ('AMD', 7947),\n",
       " ('gme', 7511),\n",
       " ('SEC', 6223),\n",
       " ('NAKD', 5448),\n",
       " ('CNBC', 4683),\n",
       " ('TSLA', 4241),\n",
       " ('NOK', 4140)]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "flat_list = [item for sublist in df['title_ner_lg'] for item in sublist]\n",
    "\n",
    "# count values using Counter\n",
    "Counter(flat_list).most_common(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4330df29-5b98-44bb-a188-e5ca86a4514b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "uri": "gcr.io/deeplearning-platform-release/base-cpu:m103"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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