LDA_group_topics_pos_neg_new-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import glob\n",
    "import nltk\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('all_data_cleaned_new.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set Polarity, Subjectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in df.iterrows():\n",
    "    blob = TextBlob(str(row.message_text))\n",
    "    sentiment = blob.sentiment\n",
    "    df.set_value(i,'polarity', float(sentiment.polarity))\n",
    "    df.set_value(i,'subjectivity', float(sentiment.subjectivity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pos = 0\n",
    "neg = 0\n",
    "neu = 0\n",
    "for i, row in df.iterrows():\n",
    "    if row.polarity >= 0.2:\n",
    "        pos = pos +1\n",
    "    elif row.polarity <= -0.2:\n",
    "        neg = neg + 1\n",
    "    else:\n",
    "        neu = neu + 1\n",
    "        \n",
    "print('pos:', pos)\n",
    "print('neg:', neg)\n",
    "print('neu:', neu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('pos_neg_new.csv')"
   ]
  }
 ],
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