Hobbit Or There And Back Again - Text analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Hobbit - Or there and back again\n",
    "\n",
    "### Book analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import sent_tokenize\n",
    "import nltk\n",
    "from nltk.corpus import words\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "\n",
    "import string\n",
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from operator import itemgetter\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "text = open('hobbit.txt', 'r').read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\n\\n\\n\\n\\n\\nTHE HOBBIT \\n\\n\\n\\nOR \\n\\nTHERE AND BACK \\nAGAIN \\n\\n\\n\\nJ.R.R. TOLKIEN \\n\\n\\n\\nThe Hobbit is a tale of high'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = text.replace('\\n', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = nltk.sent_tokenize(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4915"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TOLKIEN The Hobbit is a tale of high adventure, undertaken by a company of dwarves, in search of dragon-guarded gold.'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Remove some annotations\n",
    "sentences = sentences[34:4904]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'You are a very fine person, Mr. Baggins, and I am very fond of you; but you are only quite a little fellow in a wide world after all!” “Thank goodness!” said Bilbo laughing, and handed him the tobacco-jar.'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Chapter I AN UNEXPECTED PARTY In a hole in the ground there lived a hobbit.'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sid = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "compound_scores = []\n",
    "for sentence in sentences:\n",
    "    compound_scores.append((sentence, sid.polarity_scores(sentence)['compound'],\n",
    "                            sid.polarity_scores(sentence)['pos'],\n",
    "                           sid.polarity_scores(sentence)['neg'],\n",
    "                           sid.polarity_scores(sentence)['neu']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Not a nasty, dirty, wet hole, filled with the ends of worms and an oozy smell, nor yet a dry, bare, sandy hole with nothing in it to sit down on or to eat: it was a hobbit-hole, and that means comfort.',\n",
       " 0.7802,\n",
       " 0.179,\n",
       " 0.0,\n",
       " 0.821)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compound_scores[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Chapter I AN UNEXPECTED PARTY In a hole in the ground there lived a hobbit.', 0.5319, 0.238, 0.0, 0.762) \n",
      "\n",
      "('Not a nasty, dirty, wet hole, filled with the ends of worms and an oozy smell, nor yet a dry, bare, sandy hole with nothing in it to sit down on or to eat: it was a hobbit-hole, and that means comfort.', 0.7802, 0.179, 0.0, 0.821) \n",
      "\n",
      "('It had a perfectly round door like a porthole, painted green, with a shiny yellow brass knob in the exact middle.', 0.7717, 0.295, 0.0, 0.705) \n",
      "\n",
      "('The door opened on to a tube-shaped hall like a tunnel: a very comfortable tunnel without smoke, with panelled walls, and floors tiled and carpeted, provided with polished chairs, and lots and lots of pegs for hats and coats — the hobbit was fond of visitors.', 0.8399, 0.187, 0.0, 0.813) \n",
      "\n",
      "('No going upstairs for the hobbit: bedrooms, bathrooms, cellars, pantries (lots of these), wardrobes (he had whole rooms devoted to clothes), kitchens, dining-rooms, all were on the same floor, and indeed on the same passage.', 0.128, 0.071, 0.058, 0.871) \n",
      "\n",
      "('The best rooms were all on the left-hand side (going in), for these were the only ones to have windows, deep-set round windows looking over his garden, and meadows beyond, sloping down to the river.', 0.6369, 0.11, 0.0, 0.89) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for el in compound_scores:\n",
    "    if el[1] > 0:\n",
    "        print(el, '\\n')\n",
    "        i += 1\n",
    "    if i > 5:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('The tunnel wound on and on, going fairly but not quite straight into the side of the hill — The Hill, as all the people for many miles round called it — and many little round doors opened out of it, first on one side and then on another.', -0.3235, 0.0, 0.048, 0.952) \n",
      "\n",
      "('I suppose hobbits need some description nowadays, since they have become rare and shy of the Big People, as they call us.', -0.25, 0.0, 0.091, 0.909) \n",
      "\n",
      "('Hobbits have no beards.', -0.296, 0.0, 0.423, 0.577) \n",
      "\n",
      "('He had not been down that way under The Hill for ages and ages, not since his friend the Old Took died, in fact, and the hobbits had almost forgotten what he looked like.', -0.6525, 0.062, 0.194, 0.744) \n",
      "\n",
      "('But Gandalf looked at him from under long bushy eyebrows that stuck out further than the brim of his shady hat.', -0.25, 0.0, 0.091, 0.909) \n",
      "\n",
      "('“But I have no time to blow smoke-rings this morning.', -0.296, 0.0, 0.216, 0.784) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for el in compound_scores:\n",
    "    if el[1] < 0:\n",
    "        print(el, '\\n')\n",
    "        i += 1\n",
    "    if i > 5:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Most negative compound score assigned to: \n",
      " ('There were lots of dragons in the North in those days, and gold was probably getting scarce up there, with the dwarves flying south or getting killed, and all the general waste and destruction that dragons make going from bad to worse.', -0.9559, 0.0, 0.322, 0.678)\n",
      "\n",
      "Most positive compound score was assigned to: \n",
      " ('Beorn was most jolly for a change; indeed he seemed to be in a splendidly good humour and set them all laughing with his funny stories; nor did they have to wonder long where he had been or why he was so nice to them, for he told them himself.', 0.9698, 0.353, 0.0, 0.647)\n"
     ]
    }
   ],
   "source": [
    "all_scores = []\n",
    "for el in compound_scores:\n",
    "    all_scores.append(el[1])\n",
    "\n",
    "for el in compound_scores:\n",
    "    if el[1] == max(all_scores):\n",
    "        print('\\nMost positive compound score was assigned to: \\n', el)\n",
    "    elif el[1] == min(all_scores):\n",
    "        print('\\nMost negative compound score assigned to: \\n', el)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(compound_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>The door opened on to a tube-shaped hall like ...</td>\n",
       "      <td>0.8399</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The tunnel wound on and on, going fairly but n...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No going upstairs for the hobbit: bedrooms, ba...</td>\n",
       "      <td>0.1280</td>\n",
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       "      <td>0.058</td>\n",
       "      <td>0.871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>The best rooms were all on the left-hand side ...</td>\n",
       "      <td>0.6369</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>This hobbit was a very well-to-do hobbit, and ...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>The Bagginses had lived in the neighbourhood o...</td>\n",
       "      <td>0.5262</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>This is a story of how a Baggins had an advent...</td>\n",
       "      <td>0.3182</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   0       1      2      3  \\\n",
       "0  Chapter I AN UNEXPECTED PARTY In a hole in the...  0.5319  0.238  0.000   \n",
       "1  Not a nasty, dirty, wet hole, filled with the ...  0.7802  0.179  0.000   \n",
       "2  It had a perfectly round door like a porthole,...  0.7717  0.295  0.000   \n",
       "3  The door opened on to a tube-shaped hall like ...  0.8399  0.187  0.000   \n",
       "4  The tunnel wound on and on, going fairly but n... -0.3235  0.000  0.048   \n",
       "5  No going upstairs for the hobbit: bedrooms, ba...  0.1280  0.071  0.058   \n",
       "6  The best rooms were all on the left-hand side ...  0.6369  0.110  0.000   \n",
       "7  This hobbit was a very well-to-do hobbit, and ...  0.0000  0.000  0.000   \n",
       "8  The Bagginses had lived in the neighbourhood o...  0.5262  0.111  0.041   \n",
       "9  This is a story of how a Baggins had an advent...  0.3182  0.119  0.000   \n",
       "\n",
       "       4  \n",
       "0  0.762  \n",
       "1  0.821  \n",
       "2  0.705  \n",
       "3  0.813  \n",
       "4  0.952  \n",
       "5  0.871  \n",
       "6  0.890  \n",
       "7  1.000  \n",
       "8  0.848  \n",
       "9  0.881  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['chapter'] = np.where(df[0].str.startswith('Chapter'),\n",
    "                         df[0].str.split(' ').str[:2], np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Chapter I AN UNEXPECTED PARTY In a hole in the...</td>\n",
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       "      <td>It had a perfectly round door like a porthole,...</td>\n",
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       "      <th>3</th>\n",
       "      <td>The door opened on to a tube-shaped hall like ...</td>\n",
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       "      <td>The tunnel wound on and on, going fairly but n...</td>\n",
       "      <td>-0.3235</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.952</td>\n",
       "      <td>[Chapter, I]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   0       1      2      3  \\\n",
       "0  Chapter I AN UNEXPECTED PARTY In a hole in the...  0.5319  0.238  0.000   \n",
       "1  Not a nasty, dirty, wet hole, filled with the ...  0.7802  0.179  0.000   \n",
       "2  It had a perfectly round door like a porthole,...  0.7717  0.295  0.000   \n",
       "3  The door opened on to a tube-shaped hall like ...  0.8399  0.187  0.000   \n",
       "4  The tunnel wound on and on, going fairly but n... -0.3235  0.000  0.048   \n",
       "\n",
       "       4       chapter  \n",
       "0  0.762  [Chapter, I]  \n",
       "1  0.821  [Chapter, I]  \n",
       "2  0.705  [Chapter, I]  \n",
       "3  0.813  [Chapter, I]  \n",
       "4  0.952  [Chapter, I]  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='ffill', inplace=True)\n",
    "df.dropna(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.drop(df.loc[df[0].str.startswith('Chapter')].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.columns = ['sentences', 'compound_score', 'pos_score', 'neg_score', 'neu_score', 'chapter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['chapter'] = df['chapter'].str[0] + ' ' + df['chapter'].str[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>sentences</th>\n",
       "      <th>compound_score</th>\n",
       "      <th>pos_score</th>\n",
       "      <th>neg_score</th>\n",
       "      <th>neu_score</th>\n",
       "      <th>chapter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not a nasty, dirty, wet hole, filled with the ...</td>\n",
       "      <td>0.7802</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.821</td>\n",
       "      <td>Chapter I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>It had a perfectly round door like a porthole,...</td>\n",
       "      <td>0.7717</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.705</td>\n",
       "      <td>Chapter I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The door opened on to a tube-shaped hall like ...</td>\n",
       "      <td>0.8399</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.813</td>\n",
       "      <td>Chapter I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The tunnel wound on and on, going fairly but n...</td>\n",
       "      <td>-0.3235</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.952</td>\n",
       "      <td>Chapter I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No going upstairs for the hobbit: bedrooms, ba...</td>\n",
       "      <td>0.1280</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.871</td>\n",
       "      <td>Chapter I</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           sentences  compound_score  \\\n",
       "1  Not a nasty, dirty, wet hole, filled with the ...          0.7802   \n",
       "2  It had a perfectly round door like a porthole,...          0.7717   \n",
       "3  The door opened on to a tube-shaped hall like ...          0.8399   \n",
       "4  The tunnel wound on and on, going fairly but n...         -0.3235   \n",
       "5  No going upstairs for the hobbit: bedrooms, ba...          0.1280   \n",
       "\n",
       "   pos_score  neg_score  neu_score    chapter  \n",
       "1      0.179      0.000      0.821  Chapter I  \n",
       "2      0.295      0.000      0.705  Chapter I  \n",
       "3      0.187      0.000      0.813  Chapter I  \n",
       "4      0.000      0.048      0.952  Chapter I  \n",
       "5      0.071      0.058      0.871  Chapter I  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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R7EAvYH46LJspvdplIKUKFgfg/JVg3p071ssRZVyfLZrCuZCLAJQqWJxnOvRO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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.plot(df.groupby('chapter').mean()['compound_score'].index,\n",
    "        df.groupby('chapter').mean()['compound_score'].values, linewidth=3, color=\"#186A3B\")\n",
    "ax.set_xticklabels(df.groupby('chapter').mean()['compound_score'].index,\n",
    "                   rotation=30)\n",
    "ax.set_ylim(-0.2, 0.2)\n",
    "ax.axhline(y=0, linestyle=':', color='grey')\n",
    "ax.set_title('Mean compound sentiment score of each chapter - Hobbit', fontsize=16)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.fill_between(x=ax.get_xticks(), y1=-0.05, y2=0.05, color='grey', alpha=0.1)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.text(x=0, y=-0.03, s='neutral')\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.yaxis.grid(alpha=0.2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# dropping the pos, neg, neu scores returned from Vader and assigning them based on compound score thresholds.\n",
    "df['pos_score'] = np.where(df['compound_score'] >= 0.05, 1, 0)\n",
    "df['neg_score'] = np.where(df['compound_score'] <= -0.05, 1, 0)\n",
    "df['neu_score'] = np.where((df['compound_score'] > -0.05) &\n",
    "                           (df['compound_score'] < 0.05), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1547"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['pos_score'] == 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1383"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['neg_score'] == 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1922"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['neu_score'] == 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6uvjVwHZDL98WuGbMe54KnDqYnCQOSZI0ucNOOabrEMY648iTug5Bknplxu6REbFpRKwY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\nf0Rbwg1FsRuuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.bar(x=df.groupby('chapter').nunique()['sentences'].index, \n",
    "        height=df.groupby('chapter').nunique()['sentences'].values, alpha=0.7, color='#186A3B')\n",
    "ax.set_xticklabels(df['chapter'].unique(),\n",
    "                   rotation=30)\n",
    "ax.set_title('Sentence count per chapter - Hobbit', fontsize=16)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.yaxis.grid(alpha=0.2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I8zsC2DAizq7zvnQ6g+/7PdAR8RrKFYJzImLTSXx+N2A3gAMOOIB58+ZNcYQL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\nFziCcrHidXX8kcCewIp1+CDgBSN8vm/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      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.bar(x=df[df['pos_score'] == 1].groupby('chapter').nunique()['sentences'].index, \n",
    "        height=df[df['pos_score'] == 1].groupby('chapter').nunique()['sentences'].values / df.groupby('chapter').nunique()['sentences'] * 100, alpha=0.8,\n",
    "      label='positive', color='#186A3B')\n",
    "\n",
    "ax.bar(x=df[df['neu_score'] == 1].groupby('chapter').nunique()['sentences'].index, \n",
    "        height=df[df['neu_score'] == 1].groupby('chapter').nunique()['sentences'].values / df.groupby('chapter').nunique()['sentences'] * 100, alpha=0.3,\n",
    "       bottom=df[df['pos_score'] == 1].groupby('chapter').nunique()['sentences'].values / df.groupby('chapter').nunique()['sentences'] * 100,\n",
    "       label='neutral', color='#FCF3CF')\n",
    "\n",
    "ax.bar(x=df[df['neg_score'] == 1].groupby('chapter').nunique()['sentences'].index, \n",
    "        height=df[df['neg_score'] == 1].groupby('chapter').nunique()['sentences'].values / df.groupby('chapter').nunique()['sentences'] * 100,\n",
    "       bottom=(df[df['neu_score'] == 1].groupby('chapter').nunique()['sentences'].values / df.groupby('chapter').nunique()['sentences'] * 100) +\n",
    "       (df[df['pos_score'] == 1].groupby('chapter').nunique()['sentences'].values / df.groupby('chapter').nunique()['sentences'] * 100),\n",
    "       label='negative', color='#D4AC0D')\n",
    "\n",
    "\n",
    "ax.legend(frameon=False, bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "ax.set_xticklabels(df['chapter'].unique(),\n",
    "                   rotation=30)\n",
    "ax.set_title('Sentece sentiment by chapter - Hobbit', fontsize=16)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.set_ylabel('[%]')\n",
    "ax.yaxis.grid(alpha=0.2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['sentences_length'] = df['sentences'].apply(lambda x: len(x.split(' ')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>sentences</th>\n",
       "      <th>compound_score</th>\n",
       "      <th>pos_score</th>\n",
       "      <th>neg_score</th>\n",
       "      <th>neu_score</th>\n",
       "      <th>chapter</th>\n",
       "      <th>sentences_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not a nasty, dirty, wet hole, filled with the ...</td>\n",
       "      <td>0.7802</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chapter I</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>It had a perfectly round door like a porthole,...</td>\n",
       "      <td>0.7717</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chapter I</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The door opened on to a tube-shaped hall like ...</td>\n",
       "      <td>0.8399</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chapter I</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The tunnel wound on and on, going fairly but n...</td>\n",
       "      <td>-0.3235</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Chapter I</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No going upstairs for the hobbit: bedrooms, ba...</td>\n",
       "      <td>0.1280</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chapter I</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           sentences  compound_score  \\\n",
       "1  Not a nasty, dirty, wet hole, filled with the ...          0.7802   \n",
       "2  It had a perfectly round door like a porthole,...          0.7717   \n",
       "3  The door opened on to a tube-shaped hall like ...          0.8399   \n",
       "4  The tunnel wound on and on, going fairly but n...         -0.3235   \n",
       "5  No going upstairs for the hobbit: bedrooms, ba...          0.1280   \n",
       "\n",
       "   pos_score  neg_score  neu_score    chapter  sentences_length  \n",
       "1          1          0          0  Chapter I                42  \n",
       "2          1          0          0  Chapter I                21  \n",
       "3          1          0          0  Chapter I                46  \n",
       "4          0          1          0  Chapter I                49  \n",
       "5          1          0          0  Chapter I                35  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MdZIkSZLUMEOdJEmSJDXMUCdJkiRJDTPUSZIkSVLDDHWSJEmS1DBDnSRJkiQ1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\n/5n3jRH8lt8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.bar(x=df.groupby('chapter').nunique()['sentences_length'].index, \n",
    "        height=df.groupby('chapter').nunique()['sentences_length'].values, alpha=0.7, color='#186A3B')\n",
    "ax.set_xticklabels(df['chapter'].unique(),\n",
    "                   rotation=30)\n",
    "ax.set_title('Avg. sentence length - Hobbit', fontsize=16)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.yaxis.grid(alpha=0.2)\n",
    "ax.set_ylabel('words');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "string.punctuation += '“”‘—'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~“”‘—'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string.punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# clean senteces from punctuation\n",
    "translator = str.maketrans('', '', string.punctuation)\n",
    "\n",
    "df['cleaned_sentences'] = df['sentences'].apply(lambda x: x.translate(translator))\n",
    "df['cleaned_sentences'] = df['cleaned_sentences'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['cleaned_sentences'] = df['cleaned_sentences'].str.replace('  ', ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['cleaned_sentences'] = df['cleaned_sentences'].str.replace('  ', ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'They surprised us at night in a pass which we had to cross we were coming out of the Lands over West into these countries it is a long tale Then you had better come inside and tell me some of it if it won’t take all day said the man leading the way through a dark door that opened out of the courtyard into the house'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cleaned_sentences'].loc[1989]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(len(df)):\n",
    "    if df['cleaned_sentences'].loc[i] == '':\n",
    "        df['cleaned_sentences'].loc[i] = 'none'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### named entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['tagged_sent'] = df['cleaned_sentences'].str.split(' ').apply(lambda x: nltk.pos_tag(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Then', 'RB'),\n",
       " ('the', 'DT'),\n",
       " ('rain', 'NN'),\n",
       " ('began', 'VBD'),\n",
       " ('to', 'TO'),\n",
       " ('pour', 'VB'),\n",
       " ('down', 'RP'),\n",
       " ('worse', 'JJR'),\n",
       " ('than', 'IN'),\n",
       " ('ever', 'RB'),\n",
       " ('and', 'CC'),\n",
       " ('Oin', 'NNP'),\n",
       " ('and', 'CC'),\n",
       " ('Gloin', 'NNP'),\n",
       " ('began', 'VBD'),\n",
       " ('to', 'TO'),\n",
       " ('fight', 'VB')]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[500]['tagged_sent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Bilbo', 'NNP'),\n",
       " ('stood', 'VBD'),\n",
       " ('still', 'RB'),\n",
       " ('and', 'CC'),\n",
       " ('watched', 'VBD'),\n",
       " ('he', 'PRP'),\n",
       " ('loved', 'VBD'),\n",
       " ('smokerings', 'NNS'),\n",
       " ('and', 'CC'),\n",
       " ('then', 'RB'),\n",
       " ('he', 'PRP'),\n",
       " ('blushed', 'VBD'),\n",
       " ('to', 'TO'),\n",
       " ('think', 'VB'),\n",
       " ('how', 'WRB'),\n",
       " ('proud', 'JJ'),\n",
       " ('he', 'PRP'),\n",
       " ('had', 'VBD'),\n",
       " ('been', 'VBN'),\n",
       " ('yesterday', 'NN'),\n",
       " ('morning', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('the', 'DT'),\n",
       " ('smokerings', 'NNS'),\n",
       " ('he', 'PRP'),\n",
       " ('had', 'VBD'),\n",
       " ('sent', 'VBN'),\n",
       " ('up', 'RP'),\n",
       " ('the', 'DT'),\n",
       " ('wind', 'NN'),\n",
       " ('over', 'IN'),\n",
       " ('The', 'DT'),\n",
       " ('Hill', 'NNP')]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[205]['tagged_sent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['ne'] = df['tagged_sent'].apply(lambda x: nltk.ne_chunk(x, binary=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Tree('S', [Tree('NE', [('Bilbo', 'NNP')]), ('stood', 'VBD'), ('still', 'RB'), ('and', 'CC'), ('watched', 'VBD'), ('he', 'PRP'), ('loved', 'VBD'), ('smokerings', 'NNS'), ('and', 'CC'), ('then', 'RB'), ('he', 'PRP'), ('blushed', 'VBD'), ('to', 'TO'), ('think', 'VB'), ('how', 'WRB'), ('proud', 'JJ'), ('he', 'PRP'), ('had', 'VBD'), ('been', 'VBN'), ('yesterday', 'NN'), ('morning', 'NN'), ('of', 'IN'), ('the', 'DT'), ('smokerings', 'NNS'), ('he', 'PRP'), ('had', 'VBD'), ('sent', 'VBN'), ('up', 'RP'), ('the', 'DT'), ('wind', 'NN'), ('over', 'IN'), ('The', 'DT'), Tree('NE', [('Hill', 'NNP')])])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[205]['ne']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Tree('S', [('Then', 'RB'), ('the', 'DT'), ('rain', 'NN'), ('began', 'VBD'), ('to', 'TO'), ('pour', 'VB'), ('down', 'RP'), ('worse', 'JJR'), ('than', 'IN'), ('ever', 'RB'), ('and', 'CC'), Tree('NE', [('Oin', 'NNP')]), ('and', 'CC'), Tree('NE', [('Gloin', 'NNP')]), ('began', 'VBD'), ('to', 'TO'), ('fight', 'VB')])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[500]['ne']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Tree.subtrees of Tree('S', [Tree('NE', [('Bilbo', 'NNP')]), ('stood', 'VBD'), ('still', 'RB'), ('and', 'CC'), ('watched', 'VBD'), ('he', 'PRP'), ('loved', 'VBD'), ('smokerings', 'NNS'), ('and', 'CC'), ('then', 'RB'), ('he', 'PRP'), ('blushed', 'VBD'), ('to', 'TO'), ('think', 'VB'), ('how', 'WRB'), ('proud', 'JJ'), ('he', 'PRP'), ('had', 'VBD'), ('been', 'VBN'), ('yesterday', 'NN'), ('morning', 'NN'), ('of', 'IN'), ('the', 'DT'), ('smokerings', 'NNS'), ('he', 'PRP'), ('had', 'VBD'), ('sent', 'VBN'), ('up', 'RP'), ('the', 'DT'), ('wind', 'NN'), ('over', 'IN'), ('The', 'DT'), Tree('NE', [('Hill', 'NNP')])])>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[205]['ne'].subtrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# change named entities to a list\n",
    "df['named_entities'] = ''\n",
    "\n",
    "for i in range(len(df)):\n",
    "    df['named_entities'].loc[i] = [\" \".join(w for w, t in elt) for elt in df.loc[i]['ne'] if isinstance(elt, nltk.Tree)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bilbo', 'Hill']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[205]['named_entities']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split two-word named entities\n",
    "df['named_entities'] = df['named_entities'].apply(lambda x: [el.split(' ') for el in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# flatten named entities list\n",
    "df['named_entities'] = df['named_entities'].apply(lambda x: [item for sublist in x for item in sublist])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bilbo', 'Hill']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['named_entities'].loc[205]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wordnet_lemmatizer = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordnet_lemmatizer.lemmatize('bilbo', 'v') in words.words()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordnet_lemmatizer.lemmatize('gandalf', 'v') in words.words()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# now I want to remove false entities that are common words but because of being capitalised were counted as named entities\n",
    "df['named_entities'] = df['named_entities'].apply(lambda x: [el for el in x if wordnet_lemmatizer.lemmatize(el.lower(), 'v') not in words.words()\n",
    "                                                            or el == 'Bilbo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bilbo']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['named_entities'].loc[205]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unique_ne = []\n",
    "for i in range(len(df)):\n",
    "    unique_ne += [x for x in df['named_entities'].iloc[i]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Arkenstone',\n",
       " 'Armies',\n",
       " 'Avery',\n",
       " 'BagEnd',\n",
       " 'Baggins',\n",
       " 'Bagginses',\n",
       " 'Balin',\n",
       " 'Balins',\n",
       " 'Barrelrider',\n",
       " 'Beorn',\n",
       " 'Bert',\n",
       " 'Berts',\n",
       " 'Bifur',\n",
       " 'Bilbo',\n",
       " 'Bilbos',\n",
       " 'Blimey',\n",
       " 'Bofur',\n",
       " 'Bolg',\n",
       " 'Bombur',\n",
       " 'Bomburand',\n",
       " 'Bullroarer',\n",
       " 'Bywater',\n",
       " 'Calamities',\n",
       " 'Carrock',\n",
       " 'Chestnuts',\n",
       " 'Co',\n",
       " 'Dori',\n",
       " 'Dorwinion',\n",
       " 'Dragonfire',\n",
       " 'Durin',\n",
       " 'Dwalin',\n",
       " 'Dwarfmail',\n",
       " 'Dwarves',\n",
       " 'Elrond',\n",
       " 'Elvenking',\n",
       " 'Elvenkings',\n",
       " 'Esgaroth',\n",
       " 'Fa1',\n",
       " 'Fili',\n",
       " 'Follovy',\n",
       " 'Galion',\n",
       " 'Gandalf',\n",
       " 'Gandalfs',\n",
       " 'Girion',\n",
       " 'Gloin',\n",
       " 'Goblincleaver',\n",
       " 'Goblins',\n",
       " 'Gollum',\n",
       " 'Gondolin',\n",
       " 'Goodbye',\n",
       " 'Greatest',\n",
       " 'Grubb',\n",
       " 'Halls',\n",
       " 'Hes',\n",
       " 'Hmmm',\n",
       " 'Hobbit',\n",
       " 'Hobbiton',\n",
       " 'Huggins',\n",
       " 'Hullo',\n",
       " 'Ive',\n",
       " 'Kili',\n",
       " 'Lakemen',\n",
       " 'Laketown',\n",
       " 'Lonelands',\n",
       " 'Longbeards',\n",
       " 'Luckwearer',\n",
       " 'Lumme',\n",
       " 'Marshes',\n",
       " 'Maytime',\n",
       " 'Mirkwood',\n",
       " 'Moria',\n",
       " 'Mountainpath',\n",
       " 'Mountains',\n",
       " 'Nolegs',\n",
       " 'Oakenshield',\n",
       " 'Oin',\n",
       " 'Orcrist',\n",
       " 'Ori',\n",
       " 'Others',\n",
       " 'Paths',\n",
       " 'Praps',\n",
       " 'Quicker',\n",
       " 'Ravenhill',\n",
       " 'Ringwinner',\n",
       " 'Rivendell',\n",
       " 'Roac',\n",
       " 'Roads',\n",
       " 'Sackville',\n",
       " 'Sawan',\n",
       " 'Seaelves',\n",
       " 'Sidedoor',\n",
       " 'Smaug',\n",
       " 'Sssss',\n",
       " 'Thorin',\n",
       " 'Thorins',\n",
       " 'Thror',\n",
       " 'Tom',\n",
       " 'Tookclan',\n",
       " 'Tookish',\n",
       " 'Tookishly',\n",
       " 'Tookishness',\n",
       " 'Tooks',\n",
       " 'Tralalalally',\n",
       " 'Treasurehunter',\n",
       " 'Twentysecond',\n",
       " 'Underhills',\n",
       " 'Warg',\n",
       " 'Wargs',\n",
       " 'Wheres',\n",
       " 'Wilderland',\n",
       " 'William',\n",
       " 'Williams',\n",
       " 'Wisps',\n",
       " 'Wolves',\n",
       " 'Woodelves',\n",
       " 'Yaharrihey',\n",
       " 'Youll',\n",
       " 'foRosmi'}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(unique_ne)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "592\n"
     ]
    }
   ],
   "source": [
    "ne_lengths = []\n",
    "for i in range(len(df)):\n",
    "    if len(df['named_entities'].loc[i]) == 10:\n",
    "        print(i)\n",
    "    ne_lengths.append(len(df['named_entities'].loc[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(ne_lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Soon Dwalin lay by Balin, and Fili and Kili together, and Dori and Nori and Ori all in a heap, and Oin and Gloin and Bifur and Bofur and Bombur piled uncomfortably near the fire.'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the sentence with highest number of named enitites?\n",
    "df.loc[592]['sentences']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# getting a clean list of named entities\n",
    "ne_interactions = [x for x in df['named_entities'].values if len(x) > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unique_named_entities = [item for sublist in ne_interactions for item in sublist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unique_named_entities = list(set(unique_named_entities))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Ringwinner',\n",
       " 'Bert',\n",
       " 'Beorn',\n",
       " 'Tookish',\n",
       " 'Bomburand',\n",
       " 'Luckwearer',\n",
       " 'Bombur',\n",
       " 'Marshes',\n",
       " 'Woodelves',\n",
       " 'Roac',\n",
       " 'Oakenshield',\n",
       " 'Williams',\n",
       " 'Rivendell',\n",
       " 'Calamities',\n",
       " 'Fa1',\n",
       " 'Bagginses',\n",
       " 'Others',\n",
       " 'Bofur',\n",
       " 'Thorins',\n",
       " 'Ive',\n",
       " 'Mountains',\n",
       " 'Thorin',\n",
       " 'Dwarves',\n",
       " 'Goodbye',\n",
       " 'Barrelrider',\n",
       " 'Grubb',\n",
       " 'Maytime',\n",
       " 'BagEnd',\n",
       " 'William',\n",
       " 'Wilderland',\n",
       " 'Carrock',\n",
       " 'Thror',\n",
       " 'Greatest',\n",
       " 'Mirkwood',\n",
       " 'Goblincleaver',\n",
       " 'Bilbos',\n",
       " 'Tooks',\n",
       " 'Kili',\n",
       " 'Blimey',\n",
       " 'Orcrist',\n",
       " 'Wolves',\n",
       " 'Hobbiton',\n",
       " 'Bilbo',\n",
       " 'Bullroarer',\n",
       " 'Gondolin',\n",
       " 'Laketown',\n",
       " 'Balin',\n",
       " 'Moria',\n",
       " 'Elrond',\n",
       " 'Arkenstone',\n",
       " 'Tom',\n",
       " 'Fili',\n",
       " 'Wargs',\n",
       " 'Durin',\n",
       " 'Sssss',\n",
       " 'Hullo',\n",
       " 'Bifur',\n",
       " 'Bolg',\n",
       " 'Dori',\n",
       " 'foRosmi',\n",
       " 'Lakemen',\n",
       " 'Dwalin',\n",
       " 'Longbeards',\n",
       " 'Twentysecond',\n",
       " 'Gollum',\n",
       " 'Ori',\n",
       " 'Tralalalally',\n",
       " 'Goblins',\n",
       " 'Gandalf',\n",
       " 'Warg',\n",
       " 'Gloin',\n",
       " 'Wheres',\n",
       " 'Huggins',\n",
       " 'Sackville',\n",
       " 'Baggins',\n",
       " 'Co',\n",
       " 'Oin',\n",
       " 'Esgaroth',\n",
       " 'Smaug']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_named_entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unique_named_entities = [x for x in unique_named_entities if x not in ['Others', 'Goodbye', 'Wolves', 'foRosmi', 'Sssss', 'Fal', 'Roac', 'Mountains', 'Twentysecond', 'Co', 'Wheres',\n",
    "'Tralalalally', 'Others', 'Blimey', 'Maytime']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for name in unique_named_entities:\n",
    "    df[name] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# creating a column for each name from the cleaned list\n",
    "for i in range(len(df)):\n",
    "    for name in unique_named_entities:\n",
    "        if name in df.loc[i]['named_entities']:\n",
    "            df[name].iloc[i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "435"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Bilbo'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'sentences', 'compound_score', 'pos_score', 'neg_score',\n",
       "       'neu_score', 'chapter', 'sentences_length', 'cleaned_sentences',\n",
       "       'tagged_sent', 'ne', 'named_entities', 'Ringwinner', 'Bert', 'Beorn',\n",
       "       'Tookish', 'Bomburand', 'Luckwearer', 'Bombur', 'Marshes', 'Woodelves',\n",
       "       'Oakenshield', 'Williams', 'Rivendell', 'Calamities', 'Fa1',\n",
       "       'Bagginses', 'Bofur', 'Thorins', 'Ive', 'Thorin', 'Dwarves',\n",
       "       'Barrelrider', 'Grubb', 'BagEnd', 'William', 'Wilderland', 'Carrock',\n",
       "       'Thror', 'Greatest', 'Mirkwood', 'Goblincleaver', 'Bilbos', 'Tooks',\n",
       "       'Kili', 'Orcrist', 'Hobbiton', 'Bilbo', 'Bullroarer', 'Gondolin',\n",
       "       'Laketown', 'Balin', 'Moria', 'Elrond', 'Arkenstone', 'Tom', 'Fili',\n",
       "       'Wargs', 'Durin', 'Hullo', 'Bifur', 'Bolg', 'Dori', 'Lakemen', 'Dwalin',\n",
       "       'Longbeards', 'Gollum', 'Ori', 'Goblins', 'Gandalf', 'Warg', 'Gloin',\n",
       "       'Huggins', 'Sackville', 'Baggins', 'Oin', 'Esgaroth', 'Smaug'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### named entities - relations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grammar = \"NP: {<NNP>?<CC|TO>?<VBD>?<IN>?<NNP><CC|TO>?<VBD>?<IN>?<NNP><CC|TO>?<VBD>?<IN>?<NNP>?}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cp = nltk.RegexpParser(grammar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Soon Dwalin lay by Balin, and Fili and Kili together, and Dori and Nori and Ori all in a heap, and Oin and Gloin and Bifur and Bofur and Bombur piled uncomfortably near the fire.'"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sentences'].loc[592]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['chunk'] = df['tagged_sent'].apply(lambda x: cp.parse(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Tree('S', [('Soon', 'RB'), ('Dwalin', 'NNP'), ('lay', 'NN'), Tree('NP', [('by', 'IN'), ('Balin', 'NNP'), ('and', 'CC'), ('Fili', 'NNP'), ('and', 'CC'), ('Kili', 'NNP')]), ('together', 'RB'), Tree('NP', [('and', 'CC'), ('Dori', 'NNP'), ('and', 'CC'), ('Nori', 'NNP'), ('and', 'CC'), ('Ori', 'NNP')]), ('all', 'DT'), ('in', 'IN'), ('a', 'DT'), ('heap', 'NN'), Tree('NP', [('and', 'CC'), ('Oin', 'NNP'), ('and', 'CC'), ('Gloin', 'NNP'), ('and', 'CC'), ('Bifur', 'NNP')]), Tree('NP', [('and', 'CC'), ('Bofur', 'NNP'), ('and', 'CC'), ('Bombur', 'NNP'), ('piled', 'VBD')]), ('uncomfortably', 'RB'), ('near', 'IN'), ('the', 'DT'), ('fire', 'NN')])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[592]['chunk']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['chunks'] = ''\n",
    "\n",
    "for i in range(len(df)):\n",
    "    df['chunks'].loc[i] = [\" \".join(w for w, t in elt) for elt in df.loc[i]['chunk'] if isinstance(elt, nltk.Tree)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['by Balin and Fili and Kili',\n",
       " 'and Dori and Nori and Ori',\n",
       " 'and Oin and Gloin and Bifur',\n",
       " 'and Bofur and Bombur piled']"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['chunks'].loc[592]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ne_dict = {}\n",
    "for i in range(len(df)):\n",
    "    if len(df.loc[i]['chunks']) >= 1:\n",
    "        for element in df.loc[i]['chunks']:\n",
    "            names = element.split(' ')\n",
    "            new_names = []\n",
    "            for word in names:\n",
    "                if word in unique_named_entities:\n",
    "                    new_names.append(word)\n",
    "            names = tuple(set(new_names))\n",
    "            if len(names) > 1:\n",
    "                if names not in ne_dict.keys():\n",
    "                    ne_dict[names] = 1\n",
    "                else:\n",
    "                    ne_dict[names] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Baggins', 'BagEnd'): 1,\n",
       " ('Baggins', 'Balin'): 1,\n",
       " ('Baggins', 'Bilbo'): 19,\n",
       " ('Baggins', 'Bilbo', 'BagEnd'): 1,\n",
       " ('Baggins', 'Durin'): 1,\n",
       " ('Baggins', 'Dwalin', 'Balin'): 1,\n",
       " ('Baggins', 'Thorin'): 2,\n",
       " ('Barrelrider', 'Bilbo'): 1,\n",
       " ('Beorn', 'Hullo'): 1,\n",
       " ('Bert', 'Tom'): 4,\n",
       " ('Bert', 'William'): 1,\n",
       " ('Bert', 'William', 'Ive'): 1,\n",
       " ('Bert', 'William', 'Tom'): 1,\n",
       " ('Bifur', 'Bofur'): 4,\n",
       " ('Bifur', 'Bombur', 'Bofur'): 3,\n",
       " ('Bifur', 'Bombur', 'Gloin', 'Bofur'): 1,\n",
       " ('Bifur', 'Dwalin', 'Bofur', 'Balin'): 1,\n",
       " ('Bifur', 'Gloin', 'Bofur'): 1,\n",
       " ('Bifur', 'Kili', 'Bofur'): 1,\n",
       " ('Bifur', 'Ori', 'Bofur'): 1,\n",
       " ('Bifur', 'Tom', 'Bombur'): 1,\n",
       " ('Bilbo', 'Arkenstone'): 1,\n",
       " ('Bilbo', 'Balin'): 3,\n",
       " ('Bilbo', 'Bombur'): 1,\n",
       " ('Bilbo', 'Carrock'): 1,\n",
       " ('Bilbo', 'Greatest', 'Calamities'): 1,\n",
       " ('Bilbo', 'Laketown'): 1,\n",
       " ('Bofur', 'Bilbo'): 1,\n",
       " ('Bofur', 'Bombur'): 3,\n",
       " ('Bombur', 'Dwalin'): 1,\n",
       " ('Bombur', 'Kili', 'Fili', 'Gloin'): 1,\n",
       " ('Bomburand', 'Kili', 'Fili'): 1,\n",
       " ('Dori', 'Bilbo'): 1,\n",
       " ('Dori', 'Dwalin', 'Balin'): 1,\n",
       " ('Dori', 'Ori'): 2,\n",
       " ('Dwalin', 'Balin'): 8,\n",
       " ('Dwarves', 'Bilbo'): 1,\n",
       " ('Esgaroth', 'Smaug'): 1,\n",
       " ('Fili', 'Balin'): 1,\n",
       " ('Fili', 'Bombur', 'Kili', 'Balin'): 1,\n",
       " ('Gandalf', 'Bilbo'): 6,\n",
       " ('Gandalf', 'Mirkwood'): 2,\n",
       " ('Gloin', 'Bifur', 'Oin'): 1,\n",
       " ('Gloin', 'Dori', 'Kili', 'Oin'): 1,\n",
       " ('Gloin', 'Oin'): 9,\n",
       " ('Goblincleaver', 'Orcrist'): 1,\n",
       " ('Gollum', 'Bilbo'): 2,\n",
       " ('Gollum', 'Gondolin'): 1,\n",
       " ('Kili', 'Fili'): 20,\n",
       " ('Kili', 'Fili', 'Balin'): 2,\n",
       " ('Kili', 'Fili', 'Bilbo'): 1,\n",
       " ('Oin', 'Dori', 'Ori'): 5,\n",
       " ('Oin', 'Ori'): 1,\n",
       " ('Ringwinner', 'Luckwearer'): 1,\n",
       " ('Sackville', 'Bagginses'): 1,\n",
       " ('Thorin', 'Arkenstone'): 1,\n",
       " ('Thorin', 'Balin'): 1,\n",
       " ('Thorin', 'Bifur', 'Bombur', 'Bofur'): 1,\n",
       " ('Thorin', 'Bilbo'): 2,\n",
       " ('Thorin', 'Dori'): 1,\n",
       " ('Thorin', 'Durin'): 1,\n",
       " ('Thorin', 'Esgaroth'): 1,\n",
       " ('Thorin', 'Gandalf'): 4,\n",
       " ('Thorin', 'Kili', 'Fili'): 3,\n",
       " ('Thorin', 'Moria'): 1,\n",
       " ('Thorin', 'Oakenshield'): 11,\n",
       " ('Thorin', 'Thror'): 1,\n",
       " ('Warg', 'Bilbo'): 1}"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ne_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Kili', 'Fili') 20\n",
      "('Baggins', 'Bilbo') 19\n",
      "('Thorin', 'Oakenshield') 11\n",
      "('Gloin', 'Oin') 9\n",
      "('Dwalin', 'Balin') 8\n",
      "('Gandalf', 'Bilbo') 6\n",
      "('Oin', 'Dori', 'Ori') 5\n",
      "('Bifur', 'Bofur') 4\n",
      "('Bert', 'Tom') 4\n",
      "('Thorin', 'Gandalf') 4\n",
      "('Bifur', 'Bombur', 'Bofur') 3\n",
      "('Bofur', 'Bombur') 3\n",
      "('Thorin', 'Kili', 'Fili') 3\n",
      "('Bilbo', 'Balin') 3\n",
      "('Dori', 'Ori') 2\n",
      "('Thorin', 'Bilbo') 2\n",
      "('Kili', 'Fili', 'Balin') 2\n",
      "('Gollum', 'Bilbo') 2\n",
      "('Gandalf', 'Mirkwood') 2\n",
      "('Baggins', 'Thorin') 2\n"
     ]
    }
   ],
   "source": [
    "for k, v in sorted(ne_dict.items(), key=itemgetter(1), reverse=True):\n",
    "    if v > 1:\n",
    "        print(k, v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### visualise named entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_grouped = df.groupby('chapter', as_index=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_grouped.drop(['index', 'compound_score', 'sentences_length'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Ringwinner', 'Bomburand', 'Luckwearer', 'Marshes', 'Calamities', 'Fa1',\n",
       "       'Ive', 'Barrelrider', 'Grubb', 'Wilderland', 'Greatest',\n",
       "       'Goblincleaver', 'Tooks', 'Hobbiton', 'Bullroarer', 'Longbeards',\n",
       "       'Warg', 'Huggins'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grouped.loc[:,(df_grouped.sum() == 1)].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_grouped.drop(df_grouped.loc[:,(df_grouped.sum() == 1)].columns, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['chapter', 'pos_score', 'neg_score', 'neu_score', 'Bert', 'Beorn',\n",
       "       'Tookish', 'Bombur', 'Woodelves', 'Oakenshield', 'Williams',\n",
       "       'Rivendell', 'Bagginses', 'Bofur', 'Thorins', 'Thorin', 'Dwarves',\n",
       "       'BagEnd', 'William', 'Carrock', 'Thror', 'Mirkwood', 'Bilbos', 'Kili',\n",
       "       'Orcrist', 'Bilbo', 'Gondolin', 'Laketown', 'Balin', 'Moria', 'Elrond',\n",
       "       'Arkenstone', 'Tom', 'Fili', 'Wargs', 'Durin', 'Hullo', 'Bifur', 'Bolg',\n",
       "       'Dori', 'Lakemen', 'Dwalin', 'Gollum', 'Ori', 'Goblins', 'Gandalf',\n",
       "       'Gloin', 'Sackville', 'Baggins', 'Oin', 'Esgaroth', 'Smaug'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grouped.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_grouped['Bilbo'] = df_grouped['Bilbo'] + df_grouped['Bilbos']\n",
    "df_grouped['Thorin'] = df_grouped['Thorin'] + df_grouped['Thorins']\n",
    "df_grouped.drop(['Bilbos', 'Thorins', 'Baggins'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eXn+Aey/tQ3ioo4GjV0oppZSvzZ07l5kzZ3LmzBl69uzJG2+8EeiQvBJjmkTL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\nk5kLOam7XeV5oHQC5e9r2gOcAlg+fDmP7nyUx3Y9xju93mFY8DAJgzS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cx+DFuGwt73wN4PJgRBgxHSqbUa5VCrC9TqBqe/hcySO2c9/By4HJNO4pOSHXhefZDG6vPRjGjm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\nupTyXm9sB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Sv62posVkK0LrdQxpqgaNr+cJr3N18jp0DG/D6+nPu4PZ4ekYw6PohLjy4x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\nGo1Go9GEMFrUaTQajUaj0Wg0Gk0I8//yRL4TUGydNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "for character in ['Bilbo', 'Thorin', 'Gandalf', 'Gollum', 'Smaug']:\n",
    "    df_plot = df_grouped[['chapter', character]]\n",
    "    ax.plot(df_grouped['chapter'], df_grouped[character], label=character)\n",
    "ax.legend(frameon=False)\n",
    "ax.set_title('Occurences of named entities in text - main characters in Hobbit', fontsize=16)\n",
    "ax.set_ylabel('count')\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.yaxis.grid(alpha=0.2)\n",
    "\n",
    "ax.set_xticklabels(df_grouped['chapter'], rotation=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fo+YWZLTWOWRE2gHXJyJyqBk4cCBFRUUAPP3001x77bW88MILHe7z29/+lsrK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ig9Qunn6/HVReDg5IFJX0NTUB0xd1yD4hzdJxvMVi7LKKbeYmVtQl+iFS7YbDY0Gg2VlZXHErmm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\nde50mPQ69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ofwxAW4eP5FOTK2wJanIXM5+PSzq3MhJy9ZslgsVFdXH6fCabVawK7CBQcH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\nTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "for character in ['Fili', 'Kili', 'Bifur', 'Bofur', 'Bombur', 'Balin', 'Dwalin', 'Oin', 'Gloin', 'Dori', 'Ori']:\n",
    "    df_plot = df_grouped[['chapter', character]]\n",
    "    ax.plot(df_grouped['chapter'], df_grouped[character], label=character)\n",
    "ax.legend(frameon=False)\n",
    "ax.set_title('Occurences of named entities in text - Dwarves in Hobbit', fontsize=16)\n",
    "ax.set_ylabel('count')\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.yaxis.grid(alpha=0.2)\n",
    "ax.set_xticklabels(df_grouped['chapter'], rotation=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['chapter', 'pos_score', 'neg_score', 'neu_score', 'Bert', 'Beorn',\n",
       "       'Tookish', 'Bombur', 'Woodelves', 'Oakenshield', 'Williams',\n",
       "       'Rivendell', 'Bagginses', 'Bofur', 'Thorin', 'Dwarves', 'BagEnd',\n",
       "       'William', 'Carrock', 'Thror', 'Mirkwood', 'Kili', 'Orcrist', 'Bilbo',\n",
       "       'Gondolin', 'Laketown', 'Balin', 'Moria', 'Elrond', 'Arkenstone', 'Tom',\n",
       "       'Fili', 'Wargs', 'Durin', 'Hullo', 'Bifur', 'Bolg', 'Dori', 'Lakemen',\n",
       "       'Dwalin', 'Gollum', 'Ori', 'Goblins', 'Gandalf', 'Gloin', 'Sackville',\n",
       "       'Oin', 'Esgaroth', 'Smaug'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grouped.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "top_bilbo_chars = df_grouped.iloc[:,4:].sum().sort_values(ascending=False)[0:15].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Bilbo', 'Thorin', 'Gandalf', 'Gollum', 'Smaug', 'Balin', 'Bombur',\n",
       "       'Fili', 'Beorn', 'Kili', 'Mirkwood', 'Bert', 'Dori', 'Elrond',\n",
       "       'William'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_bilbo_chars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colors = [\"#186A3B\", \"#1D8348\", \"#239B56\", \"#28B463\", \"#2ECC71\", \"#58D68D\",\n",
    "         \"#82E0AA\", \"#ABEBC6\", \"#D5F5E3\", \"#FCF3CF\", \"#F9E79F\", \"#F7DC6F\", \"#F4D03F\", \"#F1C40F\",\n",
    "         \"#D4AC0D\", \"#B7950B\", \"#9A7D0A\", \"#7D6608\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J7ZiF28/ouA1rV2za3rhuBbetWsb1qzYCcP3qjZvqjj5p7eYFKEmSpM3mjLkk\nSZLUASbmkiRJUgfMy8Q8yYYkK3o+i5IsSfLWtv7EJG9vt1+c5PmDjViSJEma2HxdY35LVS0eVXYt\nsHx0w6o6fU4ikiRJkjbDvJwxH0uSI5N8bozyU5K8YhAxSZIkSVM1XxPz7XuWsXx6pp0kWZpkeZLl\nay67rp/xSZIkSdMyXxPzW6pqcfs5ZqadVNUZVbWkqpbscvA+/YxPkiRJmpb5mphLkiRJWxQTc0mS\nJKkDTMwlSZKkDpiXr0usqp3GKDsXOLfdXgYsa7dPmbPAJEmSpBlyxlySJEnqgFTVoGPohOHh4Roa\nGhp0GJrnhoeHcRypHxxL6hfHkvrBcdRXGa/CGXNJkiSpA0zMJUmSpA6Ylw9/zoYjTj2BVetuGrNu\n293v/qubxz37qey/w16b9vfbcc9Zi20qjlm4/abtDWtX3K1+47qm7LZVyybs5/pVG7l+9cZN+0ef\ntLY/AUqSJGlCzphLkiRJHWBiLkmSJHWAibkkSZLUAbOSmCfZkGRFksuSXJrk8D71uyzJsf3oS5Ik\nSeqS2Xr485aqWgyQ5OnAG4EnztK5pizJgqraMOg4JEmSpNHmYinLzsDNAGn8a5IrkqxMclxbfmSS\nryX5WJLvJzktyQlJLm7bPbinv6ckOb9t94z2+BOTvH2kQZLPJTmy3V6b5PVJLgIOm4PrlSRJkqZt\ntmbMt0+yAlgI7A4c1Zb/AbAYOBjYBbgkyXlt3cHAfsBNwNXAu6rq0CQvA/4COLltt4hm9v3BwFeT\nPGSSWHYErqiqfxhdkWQpsBRgz6cdBA/xL1pJkiRpMGZrxvyWqlpcVfsCRwPvTxLg8cCHq2pDVa0G\nvgYc0h5zSVXdWFXrgR8B57TlK2mS8REfq6qNVfUDmgR+30li2QB8cqyKqjqjqpZU1ZJdDt5nBpcp\nSZIk9cesL2WpqgtoZsd3BTJB0/U92xt79jdy15n9Gn0K4A7uei0Le7ZvdV25JEmSum7WE/Mk+wIL\ngF8A5wHHJVmQZFfgCODiaXb5h0nu0a47fxDwPeBaYHFbvhdwaN8uQJIkSZoDs73GHJpZ8hdU1YYk\nn6Z5APMympnuv62qVW3yPlXfo1kCsxvw4qq6Nck3gGtolr1cAVzarwuRJEmS5sKsJOZVtWCc8gL+\npv30lp8LnNuzf+RYdVV14gT9njBO3U5TDlySJEkaEP/ypyRJktQBaSabNTw8XENDvi5Rm2d4eBjH\nkfrBsaR+cSypHxxHfTXuy1CcMZckSZI6wMRckiRJ6oDZeivLvHPEqSewat1Nk7bbdvexf41zz/vv\nvGn7kMc9ZtJ+9t9hr03b+4a0p7EAABaLSURBVO2455Ta9Xr4xjWTnmO0DWtXjFu3cd34dQC3rVo2\nbt31qzZy/eqNm/aPPmnttGOTJEna2jljLkmSJHWAibkkSZLUAZ1KzJNsSLIiyWVJLk1y+BSOWdt+\nfUCST8x+lJIkSVL/dW2N+S1VtRggydOBNwJPnMqBVXUDcOwsxiZJkiTNmk7NmI+yM3AzQJKdkny5\nnUVfmeSZoxsnWZTkinb7xCSfSvKFJD9I8i9zHLskSZI0LV2bMd8+yQpgIbA7cFRbfitwTFX9Osku\nwIVJzqqJ/zrSYuCRwHrge0neVlXXz2bwkiRJ0kx1bcb8lqpaXFX7AkcD708Smr+Q9IYklwNfAvYA\ndpukry9X1a+q6lbgO8A+oxskWZpkeZLlay67rr9XIkmSJE1D1xLzTarqAmAXYFfghPbro9s16Ktp\nZtUnsr5newNj/Hagqs6oqiVVtWSXg++Wt0uSJElzprOJeZJ9gQXAL4B7AT+rqtuTPIkxZr8lSZKk\n+ayra8yhWb7ygqrakORM4LNJlgMrgKsGFqEkSZI0CzqVmFfVgnHK1wCHjVO3U/v1WuCAdnsZsKyn\nzTP6G6kkSZLUX51dyiJJkiRtTUzMJUmSpA7IxK8C33oMDw/X0NDQoMPQPDc8PIzjSP3gWFK/OJbU\nD46jvsp4Fc6YS5IkSR1gYi5JkiR1QKfeyjJIR5x6AqvW3TRm3Y6P3AOAFzz1WF5z34dOq9/bVy/b\n3NDm1Ia1K2ZUd/2qjZu2H/HsX/c1JkmSpK2BM+aSJElSB5iYS5IkSR0wZ4l5kr9LcmWSy5OsSPKY\nuTq3JEmS1HVzssY8yWHAM4BHVdX6JLsA95yLc0uSJEnzwVzNmO8OrKmq9QBVtaaqbkhybZI3JLkg\nyfIkj0pydpIfJXkxQJKdknw5yaVJViZ5Zlu+KMkVIydI8ookp7Tbh7Qz8xck+dfedpIkSVIXzVVi\nfg6wV5LvJ3lHkif21F1fVYcB5wPLgGOBxwKvb+tvBY6pqkcBTwL+Pcm4L2ZvvRd4cdvvhj5ehyRJ\nkjQr5iQxr6q1wKOBpcDPgY8mObGtPqv9uhK4qKqGq+rnwK1J7k3z15HekORy4EvAHsBu452rPWao\nqr7ZFn1ogrZL25n65Wsuu27mFyhJkiRtpjl7+LOqNlTVuVX1j8BLgWe3Vevbrxt7tkf2twFOAHYF\nHl1Vi4HVwELgDu4a/8L262Sz6b0xnVFVS6pqyS4H7zPdS5IkSZL6Zk4S8yQPT9L7l3kWA1Odor4X\n8LOquj3Jk4CRDHo1cL8k902yHc3DpVTVzcBwkse27Y7f/CuQJEmSZtdc/eXPnYC3tctM7gB+SLOs\n5RlTOPZM4LNJlgMrgKsA2kT99cBFwDUj5a0XAe9Msg44F/hVn65DkiRJmhVzkphX1beAw8eoWtTT\nZhnNw58j+4t62h02Tr9vBd46RtWVVXUQQJJXAcunGbIkSZI0p+Zqxnyu/W6SV9Nc33XAiYMNR5Ik\nSZpYqmrQMXTC8PBwDQ0NDToMzXPDw8M4jtQPjiX1i2NJ/eA46qtxX1QyZ29lkSRJkjQ+E3NJkiSp\nA0zMJUmSpA7YUh/+nLZnXfl/WbPtbwYdxlZn/x323LT97F3vfPnOsbuM+SIeSZKkLZYz5pIkSVIH\nmJhLkiRJHTAniXmS3ZJ8KMnVSb6V5IIkx0zQ/sgkn2u3T0zy9rmIU5IkSRqUWU/MkwT4b+C8qnpQ\nVT0aOB7Yc+IjJUmSpK3HXMyYHwXcVlWnjxRU1XVV9bYkC5O8N8nKJN9O8qSJOkqyLMmxPftr269H\nJvlako8l+X6S05KckOTitu8Hz9rVSZIkSX0wF4n5I4BLx6l7CUBVHQg8B3hfkoUzPM/BwMuAA4Hn\nAQ+rqkOBdwF/MdYBSZYmWZ5k+U//e7wQJUmSpNk35w9/JvnPJJcluQR4PPABgKq6CrgOeNgMu76k\nqm6sqvXAj4Bz2vKVwKKxDqiqM6pqSVUt2eNZj5rhaSVJkqTNNxeJ+ZXApqy3ql4CPBnYFcg0+7qD\nNuZ27fo9e+rW92xv7NnfiO9rlyRJUsfNRWL+FWBhkv/TU7ZD+/U84ASAJA8D9ga+N0Ff1wKPbref\nCWzb10glSZKkAZn1xLyqCngW8MQk1yS5GHgf8ErgHcCCJCuBjwIntktRxvPOtp+LgccA62Y3ekmS\nJGlupMmb9eQLX1trtv3NoMPY6uy/w51vzXz2rodt2j52l8PGat55w8PDDA0NDToMbQEcS+oXx5L6\nwXHUV+Mu5fYvf0qSJEkd4EORrf9+xCv9SVCSJEkD44y5JEmS1AEm5pIkSVIHuJSldcSpJ7Bq3U2D\nDmNM2+4+O0ts7nn/nQd27sUP2n9W+t0c+++w12b3sd2tsH6mf7t2Cvbbcc+7xPnwjWtm3Ne6y47c\ntH39qo3TOvb61RO3P/qktTMJSZKkrZoz5pIkSVIHmJhLkiRJHWBiLkmSJHXArCTmSXZL8qEkVyf5\nVpILkhzTh36PTPK5qbZJsl2SLyVZkeS4zT2/JEmSNFv6/vBnkgD/Dbyvqv64LdsH+P1+n2sKHgls\nW1WLB3BuSZIkacpmY8b8KOC2qjp9pKCqrquqtyVZlOT8JJe2n8Nh0yz3uUk+keSqJGe2CT5Jjm7L\nvg78wUifSQ5N8s0k326/Prw3iCT3Az4ILG5nzB88C9cqSZIk9cVsJOaPAC4dp+5nwFOr6lHAccBb\ne+oeCZwM7A88CHhckoXAO4HfA54A3L+n/VXAEVX1SOAfgDf0nqiqfgb8KXB+VS2uqh+NDibJ0iTL\nkyxfc9l1079SSZIkqU9m/eHPJP+Z5LIklwDbAu9MshL4OE0SPuLiqvpJVW0EVgCLgH2Ba6rqB1VV\nNDPgI+4FfDzJFcCbaX4gmJaqOqOqllTVkl0O3mdG1ydJkiT1w2wk5lcCjxrZqaqXAE8GdgX+ClgN\nHAwsAe7Zc9z6nu0N3Ln+vcY5z6nAV6vqAJoZ9Vn8sy6SJEnS7JqNxPwrwMIk/6enbIf2672AG9tZ\n8ecBCybp6yrggT3rw5/TU3cv4Kft9ombFbEkSZI0YH1PzNslJ88CnpjkmiQXA+8DXgm8A3hBkguB\nhwHrJunrVmAp8Pn24c/eheD/ArwxyTeYPMGXJEmSOq3vr0sEqKobgePHqT6oZ/vVbftzgXN7jn9p\nz/YXaNaajz7HBTTJ/Yi/H93X6H4lSZKkrvIvf0qSJEkdkGbliYaHh2toaGjQYWieGx4exnGkfnAs\nqV8cS+oHx1FfZbwKZ8wlSZKkDjAxlyRJkjpgVh7+nI+OOPUEVq27adBhbLLt7v39ddE9779zX8+7\n+EH732V//x32mlY8++2457Taz8R0Y3r4xjWbfc4Nv7mdDdl2s/uZ8BxrV0zaZuO6ydtM91xTOe9c\nuX7Vxrk93+qpn+/ok9bOYiSSpC2ZM+aSJElSB5iYS5IkSR0wp0tZktwX+HK7e39gA/BzYBFwQ1Xt\nP86hU+n7xcBvqur9mxunJEmSNNfmNDGvql8AiwGSnAKsrap/S7II+NxM+02yTVWd3o8YJUmSpEHo\n0sOfC5K8Ezgc+CnwzKq6Jcli4HRgB+BHwJ9U1c1JzgW+CTwOOCvJEHcm+ucCFwFPAu4NvKiqzp/z\nK5IkSZKmqEtrzB8K/GdVPQL4JfDstvz9wCur6iBgJfCPPcfcu6qeWFX/PkZ/21TVocDJo46RJEmS\nOqdLifk1VTXyPrZvAYuS3Ism+f5aW/4+4IieYz46QX+f6u1rrAZJliZZnmT5msuum3nkkiRJ0mbq\nUmK+vmd7A1NbZrNuCv2N21dVnVFVS6pqyS4H7zO1KCVJkqRZ0KXE/G6q6lfAzUme0BY9D/jaBIdI\nkiRJ81KXHv4czwuA05PsAFwNvHDA8UiSJEl9N7DEvKpO6dm+FjigZ//ferZXAI8d4/gjJ+jvyJ7t\nNYyzxlySJEnqik4vZZEkSZK2FqmqQcfQCcPDwzU0NDToMDTPDQ8P4zhSPziW1C+OJfWD46ivMl6F\nM+aSJElSB5iYS5IkSR1gYi5JkiR1wHx4XeKcOOLUE1i17qZBhzEj2+6+Zaz5uuf9d55y29m45umc\nfzy73L4Da7b9TR+i2Xrsv8Oegw6hL/bfYa++9rfdrbB+4eb3s9+OW8b9nYl+f09m28M3rpmVfjf8\n5nY2ZNtZ6XuubVi7YvJGE9i4bvOOH6TbVi0b6PnX3bE7tc2NAFy/auNm9XX96s07vh+OPmntoEMY\nkzPmkiRJUgeYmEuSJEkd0KnEPMmGJCuSXJbk0iSHt+UPSPKJdvvIJJ9rt09J8opBxixJkiT1Q9fW\nmN9SVYsBkjwdeCPwxKq6ATh2oJFJkiRJs6hTM+aj7AzcDJBkUZIrxml3cJKvJPlBkj9r2yfJvya5\nIsnKJMfNVdCSJEnSTHRtxnz7JCuAhcDuwFFTOOYg4LHAjsC3k3weOAxYDBwM7AJckuS8qrqx98Ak\nS4GlAHs+7SB4yJbxdhNJkiTNP12bMb+lqhZX1b7A0cD7k4z7Z0tbn6mqW6pqDfBV4FDg8cCHq2pD\nVa0GvgYcMvrAqjqjqpZU1ZJdDt6nz5ciSZIkTV3XEvNNquoCmtnuXSdrOsb+ZMm8JEmS1CmdTcyT\n7AssAH4xSdNnJlmY5L7AkcAlwHnAcUkWJNkVOAK4eDbjlSRJkjZHV9eYQzPr/YKq2jDJapaLgc8D\newOnVtUNST5Ns878MpoZ9L+tqlWzGLckSZK0WTqVmFfVgnHKrwUOaLfPBc5tt08Zp30Bf9N+JEmS\npM7r7FIWSZIkaWuSZnJZw8PDNTTk6xK1eYaHh3EcqR8cS+oXx5L6wXHUV+Ou0XbGXJIkSeoAE3NJ\nkiSpA0zMJUmSpA4wMZckSZI6wMRckiRJ6gATc0mSJKkDTMwlSZKkDjAxlyRJkjrAxFySJEnqABNz\nSZIkqQNMzCVJkqQOSFUNOoZOSLK0qs4YdBya3xxH6hfHkvrFsaR+cBzNDWfM77R00AFoi+A4Ur84\nltQvjiX1g+NoDpiYS5IkSR1gYi5JkiR1gIn5nVw3pX5wHKlfHEvqF8eS+sFxNAd8+FOSJEnqAGfM\nJUmSpA4wMZckSZI6YKtPzJMcneR7SX6Y5FWDjkfdluQ9SX6W5Iqest9K8sUkP2i/3qen7tXt2Ppe\nkqcPJmp1TZK9knw1yXeTXJnkZW25Y0nTkmRhkouTXNaOpde15Y4lTVuSBUm+neRz7b7jaI5t1Yl5\nkgXAfwK/DewPPCfJ/oONSh23DDh6VNmrgC9X1UOBL7f7tGPpeOAR7THvaMecdAfw11W1H/BY4CXt\neHEsabrWA0dV1cHAYuDoJI/FsaSZeRnw3Z59x9Ec26oTc+BQ4IdVdXVV3QZ8BHjmgGNSh1XVecBN\no4qfCbyv3X4f8Kye8o9U1fqqugb4Ic2Y01auqm6sqkvb7WGafwj3wLGkaarG2nZ32/ZTOJY0TUn2\nBH4XeFdPseNojm3tifkewPU9+z9py6Tp2K2qboQm4QLu15Y7vjSpJIuARwIX4VjSDLTLD1YAPwO+\nWFWOJc3EfwB/C2zsKXMczbGtPTHPGGW+P1L94vjShJLsBHwSOLmqfj1R0zHKHEsCoKo2VNViYE/g\n0CQHTNDcsaS7SfIM4GdV9a2pHjJGmeOoD7b2xPwnwF49+3sCNwwoFs1fq5PsDtB+/Vlb7vjSuJJs\nS5OUn1lVn2qLHUuasar6JXAuzZpfx5Km43HA7ye5lmZZ71FJPojjaM5t7Yn5JcBDkzwwyT1pHmQ4\na8Axaf45C3hBu/0C4DM95ccn2S7JA4GHAhcPID51TJIA7wa+W1Vv6qlyLGlakuya5N7t9vbAU4Cr\ncCxpGqrq1VW1Z1UtosmFvlJVz8VxNOe2GXQAg1RVdyR5KXA2sAB4T1VdOeCw1GFJPgwcCeyS5CfA\nPwKnAR9L8iLgx8AfAlTVlUk+BnyH5i0cL6mqDQMJXF3zOOB5wMp2bTDAa3Asafp2B97XvhHjHsDH\nqupzSS7AsaTN5/+T5liqXBIkSZIkDdrWvpRFkiRJ6gQTc0mSJKkDTMwlSZKkDjAxlyRJkjrAxFyS\nJEnqABNzSZIkqQNMzCVJkqQOMDGXJEmSOsDEXJIkSeoAE3NJkiSpA0zMJUmSpA4wMZckSZI6wMRc\nkiRJ6gATc0mSJKkDTMwlSZKkDjAxlyRJkjrAxFySJEnqABNzSZIkqQNMzCVJkqQOMDGXJEmSOsDE\nXJIkSeoAE3NJkiSpA0zMJUmSpA4wMZckSZI6wMRckiRJ6gATc0mSJKkDTMwlSZKkDjAxlyRJkjrA\nxFySJEnqABNzSZIkqQNMzCVJkqQOMDGXJEmSOsDEXJIkSeoAE3NJkiSpA0zMJUmSpA4wMZckSZI6\nwMRckiRJ6gATc0mSJKkDTMwlSZKkDjAxlyRJkjpg0sQ8yf2TfCTJj5J8J8n/JHlYkiOTfK4fQbR9\nHd6Pvsbo+9oku4xR/ppp9jPU3oOHtvvbJlmZ5DFJzk3y9FHtT07yjiSLklzRlm26Z0lOTPL2mV+Z\nJEmStiQTJuZJAnwaOLeqHlxV+wOvAXbrcxxHAtNKzJNss5nnnFZiXlXDwKuB/2yLXgF8s6ouAj4M\nHD/qkOPbckmSJGlSk82YPwm4vapOHymoqhVVdX67u1OSTyS5KsmZbSJPkn9IckmSK5Kc0VN+bpL/\nSPLNtu7QJIuAFwN/lWRFkick2TXJJ9s+LknyuPb4U9r+zgHen+QRSS5uj7t8ZDZ7MklOA7Zvjzuz\nLXt5G9MVSU4e67iq+hiwMcnftjG/uq36BPCMJNu1fS0CHgB8fSrxSJIkSZPNOh8AfGuC+kcCjwBu\nAL4BPI4mGX17Vb0eIMkHgGcAn22P2bGqDk9yBPCeqjogyenA2qr6t/aYDwFvrqqvJ9kbOBvYrz3+\n0cDjq+qWJG8D3lJVZya5J7BgKhddVa9K8tKqWtye79HAC4HHAAEuSvK1qvr2GIefDHwXWFpVN7X9\n/SLJxcDRwGdoZss/WlXV/kwiSZIkTWhzH/68uKp+UlUbgRXAorb8SUkuSrISOIomeR/xYYCqOg/Y\nOcm9x+j3KcDbk6wAzmrbDbV1Z1XVLe32BcBrkrwS2KenfLoeD3y6qtZV1VrgU8ATxml7NHAjzQ8t\nvXqXs7iMRZIkSdMyWWJ+Jc0M9XjW92xvALZJshB4B3BsVR0IvBNY2NOuRvUxen8krsOqanH72aNd\n4w2wbtOBVR8Cfh+4BTg7yVGTXM94pjStneQBwF8ChwK/k+Sgnur/Bp6c5FHA9lV16QxjkSRJ0lZo\nssT8K8B2Sf5spCDJIUmeOMExI0n4miQ7AceOqj+u7efxwK+q6lfAMDDU0+Yc4KU951w81omSPAi4\nuqreSjOzftBY7cZxe5Jt2+3zgGcl2SHJjsAxwPljHPNm4A1V9RPg5cB/jqyfb2fazwXeg7PlkiRJ\nmqYJE/OqKpok9antqwKvBE6hWVM+3jG/pJklX0kzi3zJqCY3J/kmcDrworbss8AxIw9/0sxKL2kf\n6PwOzYOWYzkOuKJd8rIv8P6JrmeUM4DLk5zZzm4vAy4GLgLeNXp9eZKnAnsD726v87PAzcDze5p9\nGDgY+Mg04pAkSZJIk3vP0cmSc4FXVNXyOTupJEmSNA/4lz8lSZKkDpjTGXNJkiRJY3PGXJIkSeoA\nE3NJkiSpA0zMJUmSpA4wMZckSZI6wMRckiRJ6gATc0mSJKkD/j+xeYSyqicLsgAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAANoAAAAcCAYAAAAHrKFyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAUUDElA0IAFFAxJQNCDBB62/0RtM8WlmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 259.2x14.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sums_by_chapter = {}\n",
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "i = 0\n",
    "sums_by_chapter = {}\n",
    "for chap in df['chapter'].unique():\n",
    "    df_plot = df[df['chapter'] == chap]\n",
    "    for char in top_bilbo_chars:\n",
    "        df_plot2 = df_plot.groupby('chapter', as_index=False).sum()\n",
    "\n",
    "        value = df_plot2[char].values[0]\n",
    "\n",
    "        \n",
    "        \n",
    "        if char not in sums_by_chapter.keys():\n",
    "            sums_by_chapter[char] = 0\n",
    "        ax.barh(y=char, width=value, left=sums_by_chapter[char], color=colors[i])\n",
    "        \n",
    "        sums_by_chapter[char] = value + sums_by_chapter[char]\n",
    "        \n",
    "    i += 1\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.xaxis.grid(alpha=0.2)\n",
    "\n",
    "ax.set_title('Total character occurances by chapter', fontsize=16)\n",
    "\n",
    "sns.palplot(colors, size=0.2)\n",
    "ax.text(s='Chapters I to XVIII', x=-40, y=-5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['pos'] = np.where(df['pos_score'] > df['neg_score'], 1, 0)\n",
    "df['neg'] = np.where(df['pos_score'] < df['neg_score'], 1, 0)\n",
    "df['neu'] = np.where(df['pos_score'] == df['neg_score'], 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ha7vxTdQyNtb5um2cbcZVVZXk7cA3gR8kOYjmAf/H0gxk8b4p1HVdklOAv01yOU3S+Vqm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\nQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "\n",
    "for char in top_bilbo_chars:\n",
    "    char_df = df[df[char] != 0].loc[:, ['pos', 'neg', 'neu']].sum() / len(df[df[char] != 0]) * 100\n",
    "    ax.barh(y=char, width=char_df.loc['pos'], color='#186A3B', label='positive')\n",
    "    ax.barh(y=char, width=char_df.loc['neu'], left=char_df.loc['pos'], color='#FCF3CF', alpha=0.3, label='neutral')\n",
    "    ax.barh(y=char, width=char_df.loc['neg'], left=char_df.loc['pos'] + char_df.loc['neu'], color='#D4AC0D', \n",
    "            label='negative')\n",
    "    \n",
    "ax.legend(['positive', 'neutral', 'negative'], frameon=False, bbox_to_anchor=(1.2,0.5))\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.set_xlabel('[%]')\n",
    "ax.set_title('Sentences with character mention divided by sentiment', fontsize=16);"
   ]
  }
 ],
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