classify_posts_liwc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import oll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37746, 64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from liwc import LIWC\n",
    "liwc_class = LIWC()\n",
    "\n",
    "#posts = pd.read_csv('posts_count_me_pol.csv.gz', compression='gzip', error_bad_lines=False)\n",
    "#posts = posts[posts['me'] > 2]\n",
    "#posts = posts[posts['polarity'] > 2]\n",
    "#posts = posts[posts.contentCount.apply(lambda x: str(x).isnumeric())]\n",
    "\n",
    "#mtx = liwc_class.build_features(posts['content'])\n",
    "#wc_vector = posts['contentCount']\n",
    "\n",
    "#data = mtx.astype(float) / wc_vector.as_matrix().astype(float)[:, np.newaxis]\n",
    "#data[np.isnan(data)] = 0\n",
    "#data[data >= 1E308] = 0\n",
    "#data.shape\n",
    "\n",
    "#columns = ['funct','pronoun','ppron','i','we','you','shehe','they','ipron','article','verb','auxverb','past','present','future','adverb','preps','conj','negate','quant','number','swear','social','family','friend','humans','affect','posemo','negemo','anx','anger','sad','cogmech','insight','cause','discrep','tentat','certain','inhib','incl','excl','percept','see','hear','feel','bio','body','health','sexual','ingest','relativ','motion','space','time','work','achieve','leisure','home','money','relig','death','assent','nonfl','filler']\n",
    "#posts_feat = pd.DataFrame(data, columns=columns)\n",
    "#posts_feat.to_csv('posts_liwc_mtx.csv.gz', compression='gzip')\n",
    "\n",
    "posts_feat = pd.read_csv('posts_liwc_mtx.csv.gz', compression='gzip')\n",
    "posts_feat.drop('Unnamed: 0', axis=1,inplace=True)\n",
    "posts_feat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['diario' 'outro']\n",
      "0    1\n",
      "1   -1\n",
      "Name: class_, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(534, 69)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus_feat = pd.read_csv('corpus_liwc_mtx.csv.gz', compression='gzip')\n",
    "\n",
    "# fix labels to binary\n",
    "def classFit(x):\n",
    "    if x['class'] == \"diario\":\n",
    "        return 1\n",
    "    else:\n",
    "        return -1\n",
    "    \n",
    "corpus_feat['class_'] = corpus_feat.apply(classFit,axis=1)\n",
    "target = corpus_feat['class_'].values\n",
    "\n",
    "print(corpus_feat['class'].values[:2])\n",
    "print(corpus_feat['class_'][:2])\n",
    "corpus_feat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(534, 64)"
      ]
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "corpus_feat.drop('Unnamed: 0', axis=1,inplace=True)\n",
    "corpus_feat.drop('confidence', axis=1,inplace=True)\n",
    "corpus_feat.drop('wc', axis=1,inplace=True)\n",
    "corpus_feat.drop('class', axis=1,inplace=True)\n",
    "# generate features matrix\n",
    "data = corpus_feat.drop('class_', 1).values\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 73.,  25.,  13.,   3.,   0.,  10.,   4.,   5.,  16.,   9.,  19.,\n",
       "         5.,   8.,  10.,   2.,   2.,  32.,  12.,   1.,   6.,   0.,   4.,\n",
       "        27.,   1.,   2.,  11.,  14.,   5.,   6.,   1.,   3.,   2.,  62.,\n",
       "         9.,   8.,   9.,   7.,   5.,  11.,  32.,   8.,  12.,   5.,   0.,\n",
       "         4.,  11.,   5.,   3.,   4.,  11.,  38.,   8.,  25.,  19.,   2.,\n",
       "         4.,   1.,   1.,   4.,   0.,   0.,   0.,   2.,   0.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = oll.oll(\"CW\", C=1)\n",
    "model.fit(data, target)\n",
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predicted = model.predict(posts_feat.values)\n",
    "scores = model.scores(posts_feat.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.4973545 ,  0.17989418,  0.0978836 ,  0.00396825,  0.01058201,\n",
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       "        0.05291005,  0.06349206,  0.03968254,  0.01719577,  0.0026455 ,\n",
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       "        0.03968254,  0.02645503,  0.00793651,  0.0026455 ,  0.06481481,\n",
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       "        0.00793651,  0.0026455 ,  0.02513228,  0.        ])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
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   ],
   "source": [
    "posts_feat.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37746"
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "len(posts_feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30421"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "sum(x == 1 for x in predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "5476"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "sum(x > 0.05 for x in scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
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