GoT_SentimentAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment Analysis\n",
    "I had initially run the entire code in my other laptop, where it ran fast without any issues. \n",
    "I couldn't run it completely in my current one as it kept hanging and giving me errors that didn't happen before.\n",
    "I hope it runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "from __future__ import division\n",
    "import urllib\n",
    "import csv\n",
    "import codecs\n",
    "from string import punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('A_game_of_thrones.txt',encoding = \"utf-8\") as f:\n",
    "    GotString1 = f.read()\n",
    "lines_list1 = GotString1.split('\\n')\n",
    "pos_sent = open(\"positive.txt\").read()\n",
    "positive_words=pos_sent.split('\\n')\n",
    "positive_counts=[]\n",
    "\n",
    "neg_sent = open('negative.txt').read()\n",
    "negative_words=neg_sent.split('\\n')\n",
    "negative_counts=[]\n",
    "\n",
    "\n",
    "for a in lines_list1:\n",
    "    positive_counter=0\n",
    "    negative_counter=0\n",
    "    \n",
    "    a_processed=a.lower()\n",
    "    \n",
    "    \n",
    "    for p in list(punctuation):\n",
    "        a_processed=a_processed.replace(p,'')\n",
    "\n",
    "    words=a_processed.split(' ')\n",
    "    word_count=len(words)\n",
    "    for word in words:\n",
    "        if word in positive_words:\n",
    "            positive_counter=positive_counter+1\n",
    "        elif word in negative_words:\n",
    "            negative_counter=negative_counter+1\n",
    "        \n",
    "    positive_counts.append(positive_counter/word_count)\n",
    "    negative_counts.append(negative_counter/word_count)\n",
    "\n",
    "output=zip(lines_list1,positive_counts,negative_counts)\n",
    "\n",
    "writer = csv.writer(open('Got1_sentiment.csv', 'w'))\n",
    "writer.writerows(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1189c22b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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rbCgBa1Q3ev06jUFDRnLo3v273X9u2lr+66yPhvIraT8dWrUiZefQTP64Z0Y4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\nPvHvROBKETnR5ewi4HjzdzXwr7jkyXQokMVisVg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118e4a128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import codecs\n",
    "with codecs.open('Got1_sentiment.csv', \"r\",encoding='utf-8', errors='ignore') as fdata:\n",
    "    df1 = pd.read_csv(fdata)\n",
    "df1 = df1.fillna(0)\n",
    "Positive1 = list(df1[\"0.0\"])\n",
    "Negative1 = list(df1[\"0.0.1\"])\n",
    "\n",
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "plt.plot(Positive1)\n",
    "plt.title('Book1 A Game of Thrones : Positive & negative sentiments')\n",
    "plt.subplot(212)\n",
    "plt.plot(Negative1)\n",
    "\n",
    "\n",
    "plt.grid(True)\n",
    "plt.xlabel('No.of Words')\n",
    "plt.ylabel('Probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'codecs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-71b501ba5377>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mcodecs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'A-Clash-of-Kings.txt'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0merrors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mGotString2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mlines_list2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGotString2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mpos_sent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"positive.txt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mpositive_words\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpos_sent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'codecs' is not defined"
     ]
    }
   ],
   "source": [
    "with codecs.open('A-Clash-of-Kings.txt',encoding = \"utf-8\",errors = \"ignore\") as f:\n",
    "    GotString2 = f.read()\n",
    "lines_list2 = GotString2.split('\\n')\n",
    "pos_sent = open(\"positive.txt\").read()\n",
    "positive_words=pos_sent.split('\\n')\n",
    "positive_counts=[]\n",
    "\n",
    "neg_sent = open('negative.txt').read()\n",
    "negative_words=neg_sent.split('\\n')\n",
    "negative_counts=[]\n",
    "\n",
    "\n",
    "for a in lines_list2:\n",
    "    positive_counter=0\n",
    "    negative_counter=0\n",
    "    \n",
    "    a_processed=a.lower()\n",
    "    \n",
    "    \n",
    "    for p in list(punctuation):\n",
    "        a_processed=a_processed.replace(p,'')\n",
    "\n",
    "    words=a_processed.split(' ')\n",
    "    word_count=len(words)\n",
    "    for word in words:\n",
    "        if word in positive_words:\n",
    "            positive_counter=positive_counter+1\n",
    "        elif word in negative_words:\n",
    "            negative_counter=negative_counter+1\n",
    "        \n",
    "    positive_counts.append(positive_counter/word_count)\n",
    "    negative_counts.append(negative_counter/word_count)\n",
    "\n",
    "print(len(positive_counts))\n",
    "\n",
    "output=zip(lines_list2,positive_counts,negative_counts)\n",
    "\n",
    "writer = csv.writer(open('Got2_sentiment.csv', 'w'))\n",
    "writer.writerows(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11886cc88>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T0pwAO+zmivv7eojojCMfUaFQvvJUJhd+80YQc0yuuydnQxFYi3pQ9usUqgcN\n46A9+ve4/+LkVfzP6Z8M5Ffcfjm4dmnSzpOZ/HHPLHD+dj9rbOngpFuSZznc/PY8np2w0tP/WEwl\nxkT8EN+9tLm1MynTnhlg+24nTlvwpOVbOPWTyYut4rNvvnnSJ5LuX/7kZAAmL9/C5AwzZNwsSmHT\n/tVLM9LuxAraVBUE97bgfmlobuPGofOS7r05c13iOl1l1tTaycm3+J/psnbbTv742iz+/r2TEvf8\n2vyfGr8i6ff3Bnf3Tm94c07SAPuWlnaGzV6fZFb5yt21nHToXoxd0j2g+kyGXU3T8d68es494SDP\n5z99Zmpi4BuSeya5zuBxb6OdDe6GRZByqVR+xhxsT4GeswgA7nnfv23RzcOuqYHPTfSusIOwcvMO\n6lKYmZ4avyLtgNrOji5Pk4Uzk8V9iG9tnW/enrUusyOfTF3Z0y7+9qx1WQ2ApmPpxp6ty7t95J1c\nbcfbAm51HsX23O4ZVwvXN/PI2OS8v3TjDl6fsTbpwPpUM3ncvfWkjRUdPJHBvONUCBBuJRpkfCIK\n8tVxtkrBg2KcP3THcP+DqU6EYN1UZ0Gq8Gs+8hmAUqoo93tJRbZyPuCYyRUmJTSpzRO/cfry1NTn\njSgUSinuTDFWEzW9fVO+OGVvPvKiWDfgyqbls6Rhu6dWSOWf07bqN7yJy/ybd4ph2p0f/MpZDONB\nxUKmKJvj0dp2HgKV7hTA9Y2tHPFn//sKXWhOXOsN6N5U9IXHKgUP3N3Zug3buW2Y+3jp/CIC/+ea\neeI+EtKL/0zrufBnZ3sXq7d0L07729vz+eJR+7Jma/e9G96cm6W0qVlY3xy6CafQXP1c9rOBvBDg\nhUnZmx0fHdtzcdrm7f5s4is2+z/lrwcZNKnXCYLO4z3Xb/Mep3HmzXyzLo1ccR4ft4x+fSrzIE10\nWKXgQVNrZ9JZv5NXbGHyivDWBWSDSPdZr3E++7cPfL2banXnz1wKprmtk2tdC4ScWzQUI02tHaGO\nRThpaPY3iJxpcVq2XP9GskIO0lu9PYWpMX7UZibiawWColChtGPd4xLFgp8Gkt849oO7UZAva4VV\nCmlIt3VBIQjbFj82RYWfaVpksXHDG3MZGpJSGO86M3rW6ugGFgsRzVGn7Yamth6NlmwIugq5t+Ju\nFOQLO9CchkLPNuhBHmzx0zNs4eGHhhSzuaJii0/zmR9+9PikpN+5LBbK9WD7ICu0nQyZvMpz6mW6\n1dfFRKnIWk/PAAAgAElEQVRMRMg3kwKM2+WCVQppSDVVtZD02G+oSLn4ofGeW1eETabB4LoM6xLS\n0ZWDUnDayLMh1cC9HyXl3LfLzYcLGzyfFRP5mIiQah1LsXPZE5MyOwoBqxRKiFIp1OsbWzn+r/52\n9syVTFNmW3JQpCVmSbME4L159YUWoWixSsFS0mSyP4+Ym33hfy7NKvFc+ckzwc7fyGUmUqkxZErq\nNQphkmpxo0VjB5otJc1HrsFhN9szHFCTjuYc3g2b69+Yy+iFxT0TzNI7sD0Fi6VEcG/hYMmeUjrz\nJN9YpWCxWMqOScu3+DqGt9jIh8xWKVgslrKjvTMW+mr9fBBkV9VssUrBYrGUJXUNwQ62KhfKRimc\ne8KBhRbBYrEUEaU4AykfQyFloxR262snWlksltImH9vQlI1SsHMNLBZLqZPuhMWwKBulYLFYLKVO\ntntiBcEqBYvFYrEksErBYrFYLAmsUrBYLBZLAqsULBaLxZLAKgWLxWKxJCgbpVBqx0xaLBZLISgb\npbB5e3jHNlosFktvpWyUgj0M3GKxWDITqVIQkfNEZJGI1InIoBTPRUTuN89ni8gpUclilYLFYrFk\nJjKlICKVwEPA+cDxwKUicrzL2fnAMebvKuDfUcmTyyHsFovFUi5E2VM4DahTSi1TSrUDQ4CLXW4u\nBp5VmonAXiJycBTCHL3/gCi8tVgsll5FlErhEMB5Avcacy+oG0TkKhGZKiJTN27M7pza279zYlbv\nWSwWS7Ew+LLILOwJSmI/aaXUo8CjAAMHDszKDlRZIay48xuhymWxWCy9jSh7CmuBwxy/DzX3grqx\nWCwWS56IUilMAY4RkSNEpC9wCTDU5WYocLmZhXQ60KiUWh+hTBaLxWJJQ2TmI6VUp4j8EngPqASe\nVErNE5GrzfPBwHDgAqAOaAGuzOTvtGnTNolItidN7AdsyvLdKLFyBcPKFQwrVzB6q1yf9ONIVBlN\n1RSRqUqpgYWWw42VKxhWrmBYuYJR7nKVzYpmi8VisWTGKgWLxWKxJCg3pfBooQXwwMoVDCtXMKxc\nwShrucpqTMFisVgs6Sm3noLFYrFY0mCVgsVisVgSlI1SyLSNd8hhHSYio0VkvojME5FrzP2bRGSt\niMw0fxc43vmzkW2RiJzruH+qiMwxz+4XEclRthXGv5kiMtXc20dEPhCRJeb/3vmUS0SOc8TJTBFp\nEpFrCxVfIvKkiDSIyFzHvdDiSET6icjL5v4kEanOQa5/iMhCs/X8GyKyl7lfLSI7HXE3OAq5PGQK\nLd1CjquXHTKtEJGZ+Ywr855X3VDw/JVAKdXr/9CL55YCRwJ9gVnA8RGGdzBwirneHViM3j78JuD3\nKdwfb2TqBxxhZK00zyYDpwMCjADOz1G2FcB+rnt/BwaZ60HAXfmWy5VW9eiFNgWJL+As4BRgbhRx\nBPwcGGyuLwFezkGurwNV5vouh1zVTncuf0KTy0Om0NItzLhyPb8b+Gs+48q49aobCp6/4n/l0lPw\ns413aCil1iulppvrZmABKXZ/dXAxMEQp1aaUWo5e4X2a6G3E91BKTVQ6hZ8FvhWByBcDz5jrZxxh\nFEKurwBLlVLpVq1HKpdSaiywJUWYYcWR06/XgK/46dGkkksp9b5SqtP8nIjeP8yTsOXyiCsvChpX\nccz7PwBeSudHRHJ51Q0Fz19xykUp+NqiOwpM1+2zwCRz61emq/+ko4voJd8h5tp9PxcUMFJEponI\nVebegap7z6l64MACyBXnEpILa6HjK06YcZR4x1TojcC+Icj4v+gWY5wjjDlkjIic6Qg7H3KFlW5R\nxNWZwAal1BLHvbzHlatuKJr8VS5KoSCIyADgP8C1Sqkm9MlyRwInA+vRXdh8c4ZS6mT0qXe/EJGz\nnA9Nq6Mg85RFb5x4EfCquVUM8dWDQsaRFyJyPdAJvGBurQcON2n9W+BFEdkjT+IUZbo5uJTkhkfe\n4ypF3ZCg0PmrXJRC3rfoFpE+6ER/QSn1OoBSaoNSqkspFQMeQ5u10sm3lmRzQM5yK6XWmv8NwBtG\nhg2mOxrvMjfkWy7D+cB0pdQGI2PB48tBmHGUeEdEqoA9gc3ZCiYiVwAXAj8yFQrG3LDZXE9D26KP\nzYdcIadb2HFVBXwHeNkhb17jKlXdQBHlr3JRCn628Q4NY797AliglLrHcd951Oi3gfjMiKHAJWbW\nwBHoM6snm+5kk4icbvy8HHgrB7l2E5Hd49foQcq5JvwfG2c/doSRF7kcJLXgCh1fLsKMI6df3wM+\njFfmQRGR84A/AhcppVoc9/cXfU46InKkkWtZPuQKOd1CiyvDV4GFSqmE6SWfceVVN1BM+SvIqHQp\n/6G36F6MbgVcH3FYZ6C7f7OBmebvAuA5YI65PxQ42PHO9Ua2RThmzAAD0YVqKfAgZhV6lnIdiZ7J\nMAuYF48HtL1xFLAEGAnsk0+5jH+7oVszezruFSS+0IppPdCBttX+JMw4AvqjTWR16BkkR+YgVx3a\nfhzPZ/FZJ981aTwTmA58Mwq5PGQKLd3CjCtz/2ngapfbvMRVhrqh4Pkr/me3ubBYLBZLgnIxH1ks\nFovFB1YpWCwWiyWBVQoWi8ViSRDZGc1Rsd9++6nq6uqs3t2xYwe77bZbuAKFgJUrGFauYFi5gtFb\n5Zo2bdompdT+GR3mMmMkwyj7k+i5tl57ighwP3qEfDZmP5BMf6eeeqrKltGjR2f9bpRYuYJh5QqG\nlSsYvVUuYKryUcdGaT56GjgvzfPz0XNujwGuQq+CtFgsFksBiUwpqMwbZV0MPGuU2ERgL9eil8hZ\nvKE563eVUr7fj8UUdQ3+w1q8oTnem8oYZkdXjKUbtyfda2nvZPWWFnor9TtitHfGAr0TJE6WbtxO\nZ1cw/4sNP3mztaOLFZt2+PJvR1tp56nGnR3UN7YWWoysae+MUb8jP3ky0nUKZsOnd5RSn0nx7B3g\nTqXUR+b3KOBPSqmpKdxehe5NcOCBB546ZMiQrOTZvn07AwYMAGDiuk4Gz27j15/txykHBh9ambCu\nk0dmt3HNKf347AHp33+rrp036jr425d24bDde+php1zzNnXxj6mtXHlCX84+rE+Su5ErO3h+QTt/\nPq0/x+1TCcAz89oYvbqT+2p2Ya/+2u87Ju1k0dYYT5+Xm13UKVex0NimuGZ0CzWHVnHFZ/r5fu/W\niTup25Y5TjbtjPH7MTs595NVXPpp//5D8cTXlPpOHprZxs9P7sdpB1V5yvXwzFYm13fxyNd2pV9l\n+k00b5mwk2WNuecpJ/mMr5+P3EFLJ77kL5Z0dPLEnDbGre3kwS/vyoC+2R1dcs4550xTSg3M5K4k\nBpqVUo9iDq0eOHCgqqmpycqf2tpa4u9OenchsJR+B1RTU3N0YL8mjFgALKP/AUdQU3NUWrdPLZsM\nbOTQYz9DzXEHpJVr1YQVMHUenbsfTE1Nsi5959VZwBr2PvxYagbq7VD+Nq0W6OQzp3yOow/YHYAr\n3h0GQLbxlEquYmHpxu0wegyrWvsFks1vnMxavQ3GjGd9127U1JwRSLZiia+ZIxcDS6ja93Bqao71\nlOvXte8BcPoXzmDPXfv0eO4krDzlJJ/x1RJA/mJJRyfXT/wQ6OTkz32eQ/feNdKwCjklNe+b1Fks\nFoslPYVUCkOBy0VzOtCouvcT750EsNSp4tqZ2VKK2C1sLFkQmflIRF4CaoD9RGQNcCPQB0ApNRgY\njt4Iqg5oAa6MSpaU8uXqQYDyFuSU4GzlsuU/XEo5PiVgLrINkNIhH/kyMqWglLo0w3MF/CKq8PNF\n9sfVhxV+gQUoEFGVjXKKznLNO5b02G0u8ohtkeWOrcb8Y3ObJRvKXilEOSU3TjYVWVqxUjyzFUC4\nlLICD9oBKGVTmSV8ylYp5NpzjqwcpREs1RPbcg6XoPb4UsZajyypKFulEBa2XBWGfPTwSh0bRb2H\nfCrwslcK+Sw4tpDmjh0czYyNIUsulK1SyKeZIJuKLKj+KBeFk68eQrnEJ9jxKEsyZasUciWqyimo\n+ijXhnNUPYbeFJ+ZBst70aeWDflorFilkCNBKpFyan1aCkdvUmwWjR1TyCP5qKfDnpJaytMlw8IO\nNGfGRpElG8pWKRRrayqdXOmelYuiyNdAcylXqEHjyCpYi5OyVQq5kk05iqLoldO8eku42JlcpUc+\nGn9lrxRybST5qZRt2bMUAtv+7z3ks/FXtkqh+OtpW6QtFkv+KVulkCvZVNl+bLfZtgjKzSwc9eeW\nU3SW07eWOnZKagngzzQUXb8k7eBzL9QUUffwepOpL1Py96JP7fXYKal5pFhn7fTC+txisZQAvpSC\niLwuIt8QkbJXIm5s5Z1fbHT7pzf1esqdfNYzfiv5h4EfAktE5E4ROS5CmfJKsU7pDKNA92aFVZyp\nVlz05vS3RIcvpaCUGqmU+hFwCrACGCkiH4vIlSLSJ0oBoyZX85FtjRUGW995Y/Nk76MoxxREZF/g\nCuCnwAzgX2gl8UEkkkVNkZcc28pLTXGnmsVS+lT5cSQibwDHAc8B31RKrTePXhaRqVEJ19vwU89n\nq6tSKRGrV7KnN8zc8tsL7gWfagkRX0oBeEwpNdx5Q0T6KaXalFIDI5Cr6AlSkIq8U2JxUKxjTEHw\n+w02X5Ye+dDffs1Ht6a4NyFMQQpFsbaS0u6SWqQy5xMbB5ZyIp/6O21PQUQOAg4BdhGRz9It2x7A\nrhHLFimFaCT5qcjSyRW0FatNID3fmb+uiSP3343+fSoD+VcM2NZtAEpccdY1NHPAHv3Zo39Jz2Up\nOTKZj85FDy4fCtzjuN8MXBeRTL2OfNRjfu3HjS0dXHD/OC448SAe/tGpEUtlKQRBFWexLuD86j1j\nOeETezDs12cWWpSyIq1SUEo9AzwjIt9VSv0nTzLllWyLQ7EUpKDbH+/s6AJg2sqtUYhT8pRXT6T4\nP3beuqZCi1BU5GMCRCbz0WVKqeeBahH5rfu5UuqeFK+VBGEV/mCVsv8EDUPpFIfashQKm/69h3ye\nfZHJfLSb+T8gakF6M0HSM19pX+oDtVH31Eo5foq//W8pZjKZjx4x/2/OjziWbPFbiZWXeSQ4ZRk/\nJawALeGTyXx0f7rnSqlfhytOAciySZjVcZwB3kk7JdVxna4O8+tHKdIb1hNETSb7c1kqwBInH+U2\nk/loWh5kKAhhVSp+fAkSVjq3YRRiWw/0fmxl3/somnUKZvaRxVJ2FMvssnxQPl9q8UMm89F9Sqlr\nReRtUuQdpdRFkUkWAW/VtdO411ouPvmQxL1SLxBpT15L83WlPJAK2VfaSqm0Mzl6k1nKnrxmyYZM\n5qPnzP9/Ri1IPnijroM36mZy8cmHFKSLHaQai6zOLvGaoDdU2ks3bmevXfqw74B+kfjfG+LIkpp8\nNOYymY+mmf9jRKQv8Cl0fbVIKdUevXjFjx/lEkgB5a08l3hXoYT5yt1jGNCvirk3n1toUSylQh71\nvN+ts78BDAaWosU7QkR+ppQaEaVwFv+k3Do7xb1yb0Uq5U9JR90i297WGW0A+Ff7pW5KtISL311S\n7wbOUUrVKKXOBs4B7s30koicJyKLRKRORAaleF4jIo0iMtP8/TWY+LmTzwIR1pRUJ3amSbj0hvj0\n+w294Vst4eP3PIVmpVSd4/cy9KZ4nohIJfAQ8DVgDTBFRIYqpea7nI5TSl3oV+CwyLU8BNmDJNCK\n5ixkyYZSbx2WuvwWS3ZEn/HT9hRE5Dsi8h1gqogMF5ErROTHwNvAlAx+nwbUKaWWmfGHIcDFoUgd\nIl1KMXn5Ft/uWzu6mLGqezO5xRu2s2VHz+EVpRTPfLyCxpYOZq9pDEXWOB1dMQBa2juZuzbYhmFh\ntA6bWzuYuzb5m2as2sq0lVtobu1I+65SiknLNme9sZeX/FNXbGF83aas/EyFAhqaWpmyYgt1DWnb\nP8nvKcVLk1fR0NQaKLw1W1tYvaWlx/0NTa0s27g9kF/FjDv9Z67eRqvZpDEbumKKKSv8l18vZqza\nmpBj085YyrRIl3dnr9lGS7u3SXDeukaaMpSNdBTNOgXgm47rDcDZ5nojsEuGdw8BVjt+rwE+n8Ld\nF0VkNrAW+L1Sap7bgYhcBVwFcOCBB1JbW5sh6PTU1tayfIWuyAfXLuXftUv54+f6c/y+mc8XeGJO\nG+PWdnLy/trtS5NX8d7s1dxTk3y8xIR1nTwyu40bh3Z/zrz589hty6Iefm7fvj3xTQvX6IxTX19P\nbW3yTqZTFu8E4Na353F42wr+Nb274pk2bSqb65LlHzt2LH0rk7NTY5vO0O0d7Rnj0SmXk9sn7WTx\n1hhPnrsrFSJs3hnjd2O0bMftXcGfP++dNabWd/LgzDYuP74vXz48+D75DS1aIba2tiZkW9Mc44bx\nOvxrTunHZw/wzta1Y2qpSKMZ1zYbhbtjB6fdPipx/+nzdvN6JcH27du59YWRPDG33dc7zri94t0d\nKd/xup+OuuU6D61ZvZra2gbPdGxr03JOmPAxe/f3Z0nOtexNWt/Jv2e1ceVn+nLULm1c8e54Tj+4\nkqtP6p9VmG8saeetpR1c//n+HLN35vKbyq+NLTH+MHYnZx5SxU9O7Mfvx+yEMaN7xPmYNR08Nbed\nq0/qx+kHd+exHR2KX4xq4ZQDKvn1Kam/44p3d3DEHhXc+MVM1WZqdrRoJTV58hTWDPBr9c+OTLOP\nrow0dJgOHK6U2i4iFwBvAsekkONR4FGAgQMHqpqamuxCe3cYADU1NczpWgJLFic6YwdWH0fNqYdm\n9OLOmWOBZvoO2BM26hbKllaFW6bZo5YAi5PunXD8CdT818E9/KytrU283zB1NcydzUEHHURNzUlJ\n7m6cMhpooT2mv+EP40cCunVz6qkDOfHQPZO+88wzz2KXvskFZWNzG4weSd8+fXvInE4uJ//7nvb/\n7LNrqKwQFtY3wZhxACxt7BkXTpaMXQYzF9B3n0OoqTk+bfipWLW5BcaOZpdd+ifC+XjpJhg/CYB9\nDzuami9U93zx3WSZvVi8oRnGj2W33XaDHd0tdD95rra2lv59DkLPx0jzjiMfpr2X7n4a6iqXwaIF\nHHLoYdTUHO+Zjv0+HgltbXzhC1/koD29K+Vs5UjFvNF1MGsR/fc7jMrOdcBONnT0T+2vjzCHrJ4G\n1HPI0cdTc2LPsuXHr7lrG2HsR2yK7UpNzZmebie9uxBYyu4HVVNTc3Tifn1jK4waxdrWqrRpvrwp\nlnX87TqtFnbs4LTTTuPoA6Ldn9Tv7KP+wE+AE4BE7lFK/W+a19YChzl+H2ruJVBKNTmuh4vIwyKy\nn1IqPDtAiRFoI+4sLDC9ZXAx6dvt+ELJ4XcWWD4IS478jHMVeEzBwXPAQeiT2MagK/hMhtYpwDEi\ncoRZ43AJMNTpQEQOErO8VEROM/Js9i9+eISdQVN5l+vWCWFmujCzVjbTXLMNP1M6ZYojv2MZVs9E\nQ6r0C+PgmDCKb9jjXGFSTGMKcY5WSn1fRC5WSj0jIi8C49K9oJTqFJFfAu8BlcCTSql5InK1eT4Y\n+B7wfyLSCewELlH5OFoopbzhusuVVAqkZ+ZTjque7lP6katgTv89zoAuZXrT1/hthBRinyeVlHfD\n8C97wlq701saEn6VQnzYfJuIfAaoBw7I9JJSajgw3HVvsOP6QeBBnzKESkcs2iTMtvUQ5IQlLwUl\nEq3ykqgDsOSE3zxUiIWMzjDDCD2UnYONH9lm6bgIvaVI+DUfPSoiewN/QZuA5gN3RSZVHohFrBRS\nUUyZpkAdsqKxI0dFkIr2pcmrOPGm9wqSFwuO45MLXS5yzpN5zNMF3/sojlLqcXM5BjgyOnF6D/k4\nUzVprNXvNhchypVN/gwrUzv9iWKjwXwozb+8OZfOmKIrorCKzSQKyRVwooVeJIaX3OWI7jvyeUaz\nr56CiOwrIg+IyHQRmSYi94nIvlELFyXZZoBFG/wvZMqJHPKXn+yTmw3WH1NWbKGtM/uFSYWgILvn\nhlyX+P2EQvbaikMNaOK9u+zNR72r++vXfDQEaAC+ix4c3gS8HJVQpUBU2SDYlNTguTgKuVNVLks2\nNPP9wRP42zvzM7otV4qtpZwPUs7KK3XzkaHQ3xEWfpXCwUqpvymllpu/W4EDoxQsanJNwKxMJ7kF\nWTTh+fF3a4uem7BwfeqeVaEKUNjhxmKK9s5YuJ4WkPbOWOhjHO2dMbpcfiqliq59HdU06TDJR7Hx\nqxTeF5FLRKTC/P0APdW011BsSj6oPE73fuyPUVfK3a3g/BLWWIBfX659eSbH3lBcO8gHraSc33rs\nDSP4xYvTvd0q1aOCz8SxN4zgJ89k2iotN3JJ9u7ZQ8VWC3STTwWaaUO8ZhFpAv4f8CLQbv6GYPYi\nsoRL1K2OUKbwBXDjVdDC/M5wy3IwwYbOWpdDSLnZsnPF60tHzK33fOdv7yzgqOuGB+5N1C7a6Pms\n0HWxNWkmk2nvo93zJUi+cefDoPkik/soVm66vczkW75aPqniIteVx9mSz1kaOROxqFGk/9MfL9d+\nZ/l+VMlTDMlevP2MYPjebk9ELhKRf5q/vJ9/EDWrtrSwfNMO3+6zzQAfLdmUVWFt7ehixeae2/nG\neXbCCgAmL9/iq3sfl2H0ogbGLt7IxyFuO50Ujuv3x0uTdzFpae9kaghbHyfCyxC37kHdePgTlm5O\nbEkOBB4n+GjJJra0xnjm4xWB3kvH5u1tvt12dsUSaZiqfuyKqVC3FgedJycty25XGqVgaWNuYzEz\nVm1NnGCnlE6DID2YBeub9AaRhvrGVr0hYgCUCj9e04cXfRh+p6TeCVyDXrQ2H7hGRO6IUrB8869R\nSzjnn7Wh+ZdqmtrLU1Zz2ROTeHPm2hRvJOOu3G5+u8eO4kkZ5PXpaxm9qIEfPDIhkJxXPjWFy5+c\nzA8fn8TEgAU8fQbtaRoZX7eJDxc2JLn6w6uz+d7gCWwIeP5AWPzm5Zl8b/AELn1sIneOWJhoca7Z\nujOQP5c9MYnf1u6kOcRjNr/5wEe+3f5r1BJ++Pgkz0p68Jil/OjxSYxZ3NOME6SR4nR541vz+O9H\nJ7I0wHkP8XLR0tHF4FltgcOP09bZxbcf/phxS3SFPHzOei57YhLPT1rp24/z/zXOlHkt0472Lr5+\n79hAcrw4eRXXDJkJRNszz2dPyO82FxcAJyulYgAi8gwwA/hzVIL1RlaZgzvWpqlwvBJ/gccsHifu\ng0GCZlFnqykdfjJoqoHmVBX/vHX6sJ6W9uzXM+QypXPeuu5DipY05Pcwm0zbI6xr9K8o4xXzRkfv\nwultvBfsTINcTG1KKRaaVnXTzuCHx3j1xKas2MKufSs54RN7pn3f3Rteu02XqVVpetOp2N7WmVOF\nuyrFYTylTpDTGvZyXKdPsRIgV6We3ZhCbmH29C/ZQz9d5ygW2qT61mzGGbINKwjuNHD6V8yzTzLh\nHLROVdmHvZJ8Q3Mbi+qDnfoHqfcZcor2/cET+Mb9mXtIxbJgzClHPnJPPta0+FUKdwAzRORp00uY\nBtwWnVilT7os62vKaBZhFmILnVhMpQ/XozYKM3MXSwXhJhZTvhR1GHGRqmemVM8WdVgx9aU7P6S1\nI/sxgaRdUrP4/DDNKbl4lYscP3xsIm/5MCVDfvN4RvOROe/gI+B04HPm9p+UUt7z1koAr8SMxRQV\naU7mygU/LVGvxE/1pvtezOV/uuByqYY6TUWjUHz9vnHUpTC7xBVfsbee3PEdZsqf9Y/RbGvpYO7N\n5wI6b33kGJTMdXdOJ4n4dnj23MSVPDdxJfefs6vXa6GEX4j+lbv85rRtSw41u/PNoPH48dLNfLx0\nMxeffEjW4UdBxp6COd9guFJqvVJqqPkraYUA3glYKAPC1+8dw30jF3s7CKv/b3Jxc2v2A6JxUaat\n2JpSITiC8d7eu0ha98nmo3D9XrN1Z2J2DMALk1Zy+ZOTww3EB5tao11x/asXZ/h2Kym6NNkoda/8\nk++pqb3F/OjEr/louoh8LrOz0qdQVdXiDdu5b+SSHvdfnLSK6kHDmLWmMaMf7p5C1Gza0e75LPU4\ng9P+Gu9tZEdYBTCppRdxk8BrUDKMUAu5p398kNcPXTGtoF6f0W02CUXmHDzJyXyU5xqjaKakAp8H\nJorIUhGZLSJzRGR2lIIVipem6Er4tmHzMzt2cN593VPZ5q5t5I4RC3u4CTKbBLTN8bo35qR8NnTW\nuh61ye3DXWFmyEBfubs26fevXppB9aBh/O6VWVQPGsari7wrfb8oFN8f/DGPj1uW1l28aL0/r54z\n7vqQf7y3kOpBwxg5f0OSu0Yz02VdYyuPjl3KpY9OZHxd9zTMVJ/802emJv1esL6Jk295n4bm1h6m\ng+8PTj2l94y7PkxaxxCE6kHDeHPG2p6D3PQ0+WRDLKbSrqy+ZUIr/5m+JpCfn73lfWat3gbAys2Z\n1++4v2HgrSOpHjSshzuvcYgXJ63iG/d3H+b4/cEfpw/PI3M/Nm45J938fo+ZeOnw6l3cMWJBYsuP\nu99fxL9rl6aVo6m1k1P/9gHbWpLLzatTVyeum1s7GHjrB0lTh68ZosudVy9yfN2m/O3OjH+lcC76\nHIUvA98ELjT/Sxavgnj9G3MBnbnSLcxxZ6SF9d2J9ujYDBWgz8aFe6GXk9+/OsufJy6cUwGXbkxd\n2OMVyLDlwacaxnHOhpmyYiu3DluQ8rmbG96cy5qtO3lotC6ANw7tXp8xd21j0syU24cvZMKyzQwe\n011Ym3b2NIuNXJCsWJ4av5xtLR2Mdq2ZANjs0ftZs3UnW01hb8xiCuZvX5npqaNzUQkL65t4a5aj\n1Y3ylb8amlrTtvC3tnTwcG0dAK9Ny6xQ3MVpk8fCOy/9d90bc5KmB09ZsTVjmF407uzgmiEzeH5i\nzzULj49b1qPse+XFR8YsY9js9QA88GFdSjfudsLmHe1Mdcn+h9e628+jFjSwaXs793zQbSp+a6ZW\n6PB0MvAAABLKSURBVGMXb+SVqatZszVZod31bndjLx/msUx7H/UXkWuBPwDnAWuVUivjf9GLV1j+\n+9GJvD1rHcsCLM7xw9KGHYFaMilRPra5QDF6UUNSIcjUYg/C4nrv1ks8825r6a5AtzgqXHdLb9rK\nrea+NzcN7bmAz829IxczelEDDU2tzF3b0+TWs7Xu/azny/Dhwg2cdPP7TEijsFMhImmnw2bLefeN\n4zcvdzcQMn1Du1n5/BNX7ykV783bQOPOjtDO5+j06Gk1+FwfA7pR89GSzCuIp6/axg1vzu1Rzm4d\ntoDRi3o2BrIlVRp+vHQzrR2p191c+7Je6Oa1JuiPr83mB67ear5N2pl6Cs8AA4E5wPnA3ZFLVGT8\n6qUZfPnuMUB4duz/TF/DmX8fndaNszJNhR/79wuTVnHlU1N4w2G/3Z7DittF9c1JreQHR6duPTlx\ntkZvcZyt0NDUxu3DFySmTP7u1VkpzTPxOH93bj1TV/prPb42dQ2n3T6KCx/4iHddm7staWhOxO2f\n/jMnqcRlSt6pK7cyebmWYcbq4C1ZrzQL006cya+bh87nh49PYk4KhZmKnz4zxeeuu5k/4t50EynS\n4GxM3D58AZc9MYk5rjE2r9BT5Sm3CSsX5Zzq1SfHL0+5A4GTzjRTlTdtT+6tOuO/GI7jPF4pdSKA\niDwB5H/qRASMWrCBicuD7bczZPKqxBkBXvzzvUX8/tzjcjIHxHslmU4s6+hS9K1MH1J8q4b1ja2M\nXtTApw7aPamLHpRz7/O3BcCUFVvo7Eov27A563vciynVI9PHf179/DRfYbv9dr930YPjk34vc5jQ\nJmfYg+nnL0zns4frNZxBC2eqSQCJVdyOR6u3tLByc4unApm9ZhsH7dmfA3bvn/L5vHVNabcraU9R\nST45fjk3fvOElO7nrm3iS0fv5+lfHIXOu1NXbuXcEw5K6ebjpZs5+9j9M/rl5qv3jGH6X74GdK/c\n3pJmkkNGWV1RuzLgKuhYTPfAa9J8y+w1jfzyxel8+uA9UvvhIwONXtRAV5c/c2CYZFIKiVpQKdVZ\nUjtQpsFP19nNoNdTD/g6eXB0nVYKOajz+43tssJHXO/IsDVE3IsRc9fzj/cWZS1TEDpjiu8PnsAe\n/f3uoNJNKttuPmfT+NlIcP02PVkgFlPUB5g4oJS/b/HqQU5dsYWB1ftw0YPj2WvXPsz869dTunty\n/HLfMsV5avwKfnja4QxIkWYi/mbYNDS3JXrUQyavStx3bjA3Y9W2rJSCUwFUeKx/WeGxmWWqKHdX\nyJc9MSmQPE+OX87Wlg4GX3aqZwNw3rom5q1r4p3ZPRs/ADvS9NjjDYIrn9JnUJxy+F6ebqMgU8k9\nyZynALqntIv5LeglDKnVYBmwekuwDdOC4pXJg7DI2PyDVF5h0ZTFOoipK7eQqhgvzGI7haioN3sH\nxZTeYTUMJizLbCN3VozbWjp4aHQdh+2TflFaEJpaO/hais3gWtq7eHXa6hRvJPOlOz9MXE9ftS1x\nHXSDuUzEGzqbXSYWr/y2bttO/ulqEAVtZ7gHrOMWg4bm1oyTSrzIZHVw4myMBz3gKBsynadQGbkE\nJUpDs3dFGyTZlm3czvrtPbv0QaevpmKaTxt8OtZt28nr09fwi3OOztmvTKQafK9vauW8+8alcF1Y\nxi/dxEUnfyLQO149yKuf9z7pLM7zk1ax9259E7/D7vmlq2uC7hibjhcnrcrsKAXLNm5H0W3D95qq\n7ea2YQuSZgZmww1vzk15f/rKrZFU0ukmQ9w3cgmP/3hg6GE6Cd7Ht2QmQD6Jd7mjJXuz3xdNC3Df\nAf3CEsYTEekxyFasTF6+hXUBFm0BPDMh+wl7YxdvZGyKba/DIl9muiAzjZzEy8mXP3VAoPdSKYTH\nxy1jlY+1F5l4c2b2p+6lw50UTlPypOXZnV8RBKsUskTbWXuWpFWbW9i8I7uMHxVbW3KvaFNN7yx3\n0tmFS42g53AUiqCKOBWz1zQy28cOAYWiK6ZocGxxXu+4bgt4+FM2WKWQJR2x1Ilz1j/STzUtBGF0\ncV/IstsfhFKbxtBbJl6UErmagkqF024flbh2bo8S9ETAbAhynoLFQRjd7duHL8jsqIx4dWqwrRgK\nzZ0jbPpZeh9WKRSQbGcu9FYyrRMoNry2CbFYShmrFCwWi8WSwCoFi8VisSSwSsFisVgsCaxSsFgs\nFkuCslEK2R6QYrFYLOVE2SgF914pFovFYulJ2SiFirL5UovFYsmeSKtKETlPRBaJSJ2IDErxXETk\nfvN8toicEpUs+dhd0GKxWEqdyJSCiFQCD6FPbDseuFREjnc5Ox84xvxdBfw7KnkyHfpisVgslmh7\nCqcBdUqpZUqpdmAIcLHLzcXAs0ozEdhLRA6OQphFZbJnisViseRClBviHQI4T+dYA3zeh5tDgKTj\nikTkKnRPggMPPJDa2trAwkxa6/9QC4vFYilG+laQVf0XhJLYJVUp9SjwKMDAgQNVTU1NYD++1BVj\n4EFjOPusswCorBD6VFbQ3hlLjDfElKJPZQVtnV3s2reKrphCoc8N7lNZkfhdIUJHV4wKEfr3qUzs\nXNgZ037t1reK1s4u+lRWeB6r2RmLIQhVFcLI0bWcddZZKKUHxPtUVNAR0/53xXR48SMEKyskIW+f\nyoqE3Ds7uuhrwhPRW+yKcRN/N6YU/aq0vCLJ3xSL6Z1fd+9XRVtnjD6VFYwaXctXz6mhokLY3tZJ\n/yodnhi5umKKygqhskJQStHWGaNvVQUdXfrbKir0xoFVFcKO9i76VAp9KyuorBBiSp9DXVVRQVWF\nJM4OFiHx3UppmSsrdBz2raygM6aoHTOGs846y4Sr3fStrKCtM2b8VlRVCB1dij6VgogkDriJH5ge\nj8e4+3gaO4nLF4+/ChE6Y7HEvXg6VIjQp1IYPWYMZ555Fn0rK1Amvju7FH2rKmjt6KJCdJxUmjzR\nGes+k7qiAqoqKpLyHOip1P2qKlGo7u9B57/KCknkjXgcOdOlrTNGv6oKxo8byxlnnUUsRsJdPP91\nxmL071OZyM/xfFJVUZHIF1WVOq6qKgRlvret03yPI3+LkEi3zpiWO6b0GcNKkZC1ysjwYe0Yzqk5\nG0GfHR0/9jPuZWVFdzmLl0vntVLdx4WKdF/HHGmtTJ6Px41Cx03cXWWF0N6p46ArpogpxUfjxnJO\nTQ0Vok90261vJV1Kx4VCx288ryRkEJ2uHV06jM6Yon+Vjte+VRWJ+Gs33xOP4/g3VlVIIm/G82M8\nDQf003XRiFFjOPfLZ9G/T7Rnn0WpFNYChzl+H2ruBXUTCn0qK+hXKT0itG9VTwta/F68AMVx/nZW\nIHH3fR3WuF37po/ayopuOfqmkKufeZ4q/d33KpEeFZr7t5Nd+nZ74PymXdD347L0rRQqzPMB/ZK/\nxx03IFSZMFOFvecuyfcqJTmO+lckf5RXvu9bISnjC5K/C6Aq6af0uJepbKVKw75pLK59KpLl0umi\nr3fr19OvqhThu+PV6Z/Ti1T5FpK/Kf5uZYUkKsbE+4nviOez9JZkd1x55e9ud5krrr6V3fm2yiN8\np1zOMDOVryDEw4jHfZVp6ADsuUsffc+nX+40jadTPF5SfWe8LkiVH5xpuGe/1Pk+bKIcU5gCHCMi\nR4hIX+ASYKjLzVDgcjML6XSgUSmV+qRri8VisUROZD0FpVSniPwSeA/dbHhSKTVPRK42zwcDw4EL\ngDqgBbgyKnksFovFkhnxOky8WBGRjUC2h93uB2wKUZywsHIFw8oVDCtXMHqrXJ9USu2fyVHJKYVc\nEJGpSqmBhZbDjZUrGFauYFi5glHuctnNHywWi8WSwCoFi8VisSQoN6XwaKEF8MDKFQwrVzCsXMEo\na7nKakzBYrFYLOkpt56CxWKxWNJglYLFYrFYEpSNUsh0tkPIYR0mIqNFZL6IzBORa8z9m0RkrYjM\nNH8XON75s5FtkYic67h/qojMMc/uF/HYTMm/bCuMfzNFZKq5t4+IfCAiS8z/vfMpl4gc54iTmSLS\nJCLXFiq+RORJEWkQkbmOe6HFkYj0E5GXzf1JIlKdg1z/EJGF5jySN0RkL3O/WkR2OuJucBRyecgU\nWrqFHFcvO2RaISIz8xlX5j2vuqHg+SuBUqrX/6FXVC8FjgT6ArOA4yMM72DgFHO9O7AYfabETcDv\nU7g/3sjUDzjCyFppnk0GTkdv3jMCOD9H2VYA+7nu/R0YZK4HAXflWy5XWtUDnyxUfAFnAacAc6OI\nI+DnwGBzfQnwcg5yfR2oMtd3OeSqdrpz+ROaXB4yhZZuYcaV6/ndwF/zGVfGrVfdUPD8Ff8rl56C\nn7MdQkMptV4pNd1cNwML0FuCe3ExMEQp1aaUWo7e9uM00WdL7KGUmqh0Cj8LfCsCkS8GnjHXzzjC\nKIRcXwGWKqXSrVqPVC6l1FhgS4oww4ojp1+vAV/x06NJJZdS6n2lVKf5ORG9qaQnYcvlEVdeFDSu\n4pj3fwC8lM6PiOTyqhsKnr/ilItS8Dq3IXJM1+2zwCRz61emq/+ko4voJd8h5tp9PxcUMFJEpok+\npwLgQNW9EWE9cGAB5IpzCcmFtdDxFSfMOEq8Yyr0RmDfEGT8X3SLMc4RxhwyRkTOdISdD7nCSrco\n4upMYINSaonjXt7jylU3FE3+KhelUBBEZADwH+BapVQT+rjRI4GT0QcJ3V0Asc5QSp2MPgr1FyJy\nlvOhaXUUZJ6y6N10LwJeNbeKIb56UMg48kJErgc6gRfMrfXA4Satfwu8KCJ75Emcokw3B5eS3PDI\ne1ylqBsSFDp/lYtSyNu5DXFEpA860V9QSr0OoJTaoJTqUkrFgMfQZq108q0l2RyQs9xKqbXmfwPw\nhpFhg+mOxrvMDfmWy3A+MF0ptcHIWPD4chBmHCXeEZEqYE9gc7aCicgVwIXAj0yFgjE3bDbX09C2\n6GPzIVfI6RZ2XFUB3wFedsib17hKVTdQRPmrXJSCn7MdQsPY754AFiil7nHcd54//W0gPjNiKHCJ\nmTVwBHAMMNl0J5tE5HTj5+XAWznItZuI7B6/Rg9SzjXh/9g4+7EjjLzI5SCpBVfo+HIRZhw5/foe\n8GG8Mg+KiJwH/BG4SCnV4ri/v4hUmusjjVzL8iFXyOkWWlwZvgosVEolTC/5jCuvuoFiyl9BRqVL\n+Q99bsNidCvg+ojDOgPd/ZsNzDR/FwDPAXPM/aHAwY53rjeyLcIxYwYYiC5US4EHMavQs5TrSPRM\nhlnAvHg8oO2No4AlwEhgn3zKZfzbDd2a2dNxryDxhVZM64EOtK32J2HGEdAfbSKrQ88gOTIHuerQ\n9uN4PovPOvmuSeOZwHTgm1HI5SFTaOkWZlyZ+08DV7vc5iWuMtQNBc9f8T+7zYXFYrFYEpSL+chi\nsVgsPrBKwWKxWCwJrFKwWCwWSwKrFCwWi8WSwCoFi8VisSSwSsHSKxERJSJ3O37/XkRuCsnvl8wW\nDr9x3NtLRDbH95gRkS8YGQ41v/cUkS0iknWZE5HtuUtvsaTHKgVLb6UN+I6I7BempyJyEPA5pdR/\nKaXujd9XSm1Dz4v/tLn1RWCG+Q96N8vJSq/y9RNOVXhSWyz+sUrB0lvpRJ9p+xv3A9H7539oWvuj\nROTwFG76i8hTovernyEi55hH7wOHmM3TznS99jHdSuCLwL2u3+ON3yeLyETpPgNhb3O/VkTuE33O\nxTVmBf4EI8OtDtkOFpGxRoa5KeSwWLLGKgVLb+Yh4Ecisqfr/gPAM0qp/0JvIHd/ind/gd6b7ET0\n9hvPiEh/9IZ9S5VSJyulxrneGU+3EjgSvap0oPn9RbTSAL3N8Z9M+HOAGx1+9FVKDVRK3Q38C/i3\nkWG9w80PgfeU3sDtJPSqWIslFKxSsPRalN598v+3d/+6DEZhHMe/x8rQK2CzSSQmfwY3wGpo/OkF\n+HMNXUVisjCgIuEuKmlLEKGLkcFKDBKmx/Ccnr7eNDRSS/0+09vTt+ecqb/0fZrnHAJrubcmgeN4\nXcFbD+TNAEdxnnvgEW+S9p06MBV71DyY2Tve7mYImAAuYkAVzKwaP3OAHwjTcpK5nqbdC6qSGb8E\nSrFGMmbel1+kJxQK0u+28X48g3+9kHl//gIwBzTi8DVQwkOim0LxW37aDuuc4UHyBOyHEJZ+vWmR\nHIWC9DUzewZO8WBoqeOdcgGKQP4xEHGsCBBCGAWG8YZkPzkH1mmHQgPYINYTzOwVeMnUARaBan6S\nqJbbJ3E/I/ghMbvAHn7spEhPKBTkP9gCsv9CWsUfv9zhX8qtw9PnQwjleM8OMBBCaOKPdFbM7KOL\ntWp4L/ur+LqB1xfqmXuWgc24/jhQprN1/CCkJl9PkJsFbkMIN8ACXnsQ6Ql1SRURkUS/FEREJFEo\niIhIolAQEZFEoSAiIolCQUREEoWCiIgkCgUREUk+AcPyXymcsCogAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1149f16a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "with codecs.open('Got2_sentiment.csv', \"r\",encoding='utf-8', errors='ignore') as fdata:\n",
    "    df2 = pd.read_csv(fdata)\n",
    "df2 = df2.fillna(0)\n",
    "Positive2 = list(df2[\"0.0\"])\n",
    "Negative2 = list(df2[\"0.0.1\"])\n",
    "\n",
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "plt.plot(Positive2)\n",
    "plt.title('Book2 A Clash of Kings: Positive & negative sentiments')\n",
    "plt.subplot(212)\n",
    "plt.plot(Negative2)\n",
    "\n",
    "\n",
    "plt.grid(True)\n",
    "plt.xlabel('No.of Words')\n",
    "plt.ylabel('Probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29451\n"
     ]
    }
   ],
   "source": [
    "with open('A-Storm-of-Swords.txt',encoding = \"utf-8\") as f:\n",
    "    GotString3 = f.read()\n",
    "lines_list3 = GotString3.split('\\n')\n",
    "pos_sent = open(\"positive.txt\").read()\n",
    "positive_words=pos_sent.split('\\n')\n",
    "positive_counts=[]\n",
    "\n",
    "neg_sent = open('negative.txt').read()\n",
    "negative_words=neg_sent.split('\\n')\n",
    "negative_counts=[]\n",
    "\n",
    "\n",
    "for a in lines_list3:\n",
    "    positive_counter=0\n",
    "    negative_counter=0\n",
    "    \n",
    "    a_processed=a.lower()\n",
    "    \n",
    "    \n",
    "    for p in list(punctuation):\n",
    "        a_processed=a_processed.replace(p,'')\n",
    "\n",
    "    words=a_processed.split(' ')\n",
    "    word_count=len(words)\n",
    "    for word in words:\n",
    "        if word in positive_words:\n",
    "            positive_counter=positive_counter+1\n",
    "        elif word in negative_words:\n",
    "            negative_counter=negative_counter+1\n",
    "        \n",
    "    positive_counts.append(positive_counter/word_count)\n",
    "    negative_counts.append(negative_counter/word_count)\n",
    "\n",
    "print(len(positive_counts))\n",
    "\n",
    "output=zip(lines_list3,positive_counts,negative_counts)\n",
    "\n",
    "writer = csv.writer(open('Got3_sentiment.csv', 'w'))\n",
    "writer.writerows(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'Got3_sentiment.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-05b9884ce829>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mcodecs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Got3_sentiment.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfdata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mdf3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mittu/anaconda3/lib/python3.6/codecs.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(filename, mode, encoding, errors, buffering)\u001b[0m\n\u001b[1;32m    893\u001b[0m         \u001b[0;31m# Force opening of the file in binary mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m         \u001b[0mmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'b'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m     \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffering\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    896\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mencoding\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Got3_sentiment.csv'"
     ]
    }
   ],
   "source": [
    "with codecs.open('Got3_sentiment.csv', \"r\",encoding='utf-8', errors='ignore') as fdata:\n",
    "    df3 = pd.read_csv(fdata)\n",
    "df3 = df3.fillna(0)\n",
    "Positive3 = list(df3[\"0.0\"])\n",
    "Negative3 = list(df3[\"0.0.1\"])\n",
    "\n",
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "plt.plot(Positive3)\n",
    "plt.title('Book2 A Clash of Kings: Positive & negative sentiments')\n",
    "plt.subplot(212)\n",
    "plt.plot(Negative2)\n",
    "\n",
    "\n",
    "plt.grid(True)\n",
    "plt.xlabel('No.of Words')\n",
    "plt.ylabel('Probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('A-Feast-for-Crows.txt',encoding = \"utf-8\") as f:\n",
    "    GotString4 = f.read()\n",
    "lines_list4 = GotString4.split('\\n')\n",
    "pos_sent = open(\"positive.txt\").read()\n",
    "positive_words=pos_sent.split('\\n')\n",
    "positive_counts=[]\n",
    "\n",
    "neg_sent = open('negative.txt').read()\n",
    "negative_words=neg_sent.split('\\n')\n",
    "negative_counts=[]\n",
    "\n",
    "\n",
    "for a in lines_list4:\n",
    "    positive_counter=0\n",
    "    negative_counter=0\n",
    "    \n",
    "    a_processed=a.lower()\n",
    "    \n",
    "    \n",
    "    for p in list(punctuation):\n",
    "        a_processed=a_processed.replace(p,'')\n",
    "\n",
    "    words=a_processed.split(' ')\n",
    "    word_count=len(words)\n",
    "    for word in words:\n",
    "        if word in positive_words:\n",
    "            positive_counter=positive_counter+1\n",
    "        elif word in negative_words:\n",
    "            negative_counter=negative_counter+1\n",
    "        \n",
    "    positive_counts.append(positive_counter/word_count)\n",
    "    negative_counts.append(negative_counter/word_count)\n",
    "\n",
    "print(len(positive_counts))\n",
    "\n",
    "output=zip(lines_list4,positive_counts,negative_counts)\n",
    "\n",
    "writer = csv.writer(open('Got4_sentiment.csv', 'w'))\n",
    "writer.writerows(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "with codecs.open('Got4_sentiment.csv', \"r\",encoding='utf-8', errors='ignore') as fdata:\n",
    "    df4 = pd.read_csv(fdata)\n",
    "df4 = df4.fillna()\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Positive4 = list(df4[\"0.0\"])\n",
    "Negative4 = list(df4[\"0.0.1\"])\n",
    "\n",
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "plt.plot(Positive4)\n",
    "plt.title('Book4 A Feast for Crows : Positive & negative sentiments')\n",
    "plt.subplot(212)\n",
    "plt.plot(Negative4)\n",
    "\n",
    "\n",
    "plt.grid(True)\n",
    "plt.xlabel('No.of Words')\n",
    "plt.ylabel('Probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "with open('A_Dance_With_Dragons.txt',encoding = \"utf-8\") as f:\n",
    "    GotString5 = f.read()\n",
    "lines_list5 = GotString5.split('\\n')\n",
    "pos_sent = open(\"positive.txt\").read()\n",
    "positive_words=pos_sent.split('\\n')\n",
    "positive_counts=[]\n",
    "\n",
    "neg_sent = open('negative.txt').read()\n",
    "negative_words=neg_sent.split('\\n')\n",
    "negative_counts=[]\n",
    "\n",
    "\n",
    "for a in lines_list5:\n",
    "    positive_counter=0\n",
    "    negative_counter=0\n",
    "    \n",
    "    a_processed=a.lower()\n",
    "    \n",
    "    \n",
    "    for p in list(punctuation):\n",
    "        a_processed=a_processed.replace(p,'')\n",
    "\n",
    "    words=a_processed.split(' ')\n",
    "    word_count=len(words)\n",
    "    for word in words:\n",
    "        if word in positive_words:\n",
    "            positive_counter=positive_counter+1\n",
    "        elif word in negative_words:\n",
    "            negative_counter=negative_counter+1\n",
    "        \n",
    "    positive_counts.append(positive_counter/word_count)\n",
    "    negative_counts.append(negative_counter/word_count)\n",
    "\n",
    "print(len(positive_counts))\n",
    "\n",
    "output=zip(lines_list5,positive_counts,negative_counts)\n",
    "\n",
    "writer = csv.writer(open('Got5_sentiment.csv', 'w'))\n",
    "writer.writerows(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "with codecs.open('Got5_sentiment.csv', \"r\",encoding='utf-8', errors='ignore') as fdata:\n",
    "    df5 = pd.read_csv(fdata)\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Positive5 = list(df5[\"0.0\"])\n",
    "Negative5 = list(df5[\"0.0.1\"])\n",
    "\n",
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "plt.plot(Positive5)\n",
    "plt.title('Book5 A Dance with Dragons : Positive & negative sentiments')\n",
    "plt.subplot(212)\n",
    "plt.plot(Negative5)\n",
    "\n",
    "\n",
    "plt.grid(True)\n",
    "plt.xlabel('No.of Words')\n",
    "plt.ylabel('Probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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