ML-For-Beginners/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Length of file: 748132 characters\n"
     ]
    }
   ],
   "source": [
    "# You should download the book text, clean it, and import it here\n",
    "with open(\"pride.txt\", encoding=\"utf8\") as f:\n",
    "    file_contents = f.read()\n",
    "print(f\"Length of file: {len(file_contents)} characters\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "book_pride = TextBlob(file_contents)\n",
    "positive_sentiment_sentences = []\n",
    "negative_sentiment_sentences = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sentence in book_pride.sentences:\n",
    "    if sentence.sentiment.polarity == 1:\n",
    "        positive_sentiment_sentences.append(sentence)\n",
    "    if sentence.sentiment.polarity == -1:\n",
    "        negative_sentiment_sentences.append(sentence)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 26 most positive sentences:\n",
      "+ “What an excellent father you have, girls,” said she, when the door wasshut.\n",
      "+ He walked here, and he walked there, fancying himself so verygreat!\n",
      "+ Mr. Darcy replied, with great intrepidity,--“Miss Elizabeth Bennet.”“Miss Elizabeth Bennet!” repeated Miss Bingley.\n",
      "+ “Your examination of Mr. Darcy is over, I presume,” said Miss Bingley;“and pray what is the result?”“I am perfectly convinced by it that Mr. Darcy has no defect.\n",
      "+ Family pride, and _filial_ pride, for he is very proud of what hisfather was, have done this.\n",
      "+ _That_ would be the greatest misfortune of all!\n",
      "+ How wonderfully these sort of things occur!\n",
      "+ She owed her greatest relief to her friend Miss Lucas, whooften joined them, and good-naturedly engaged Mr. Collins’s conversationto herself.\n",
      "+ The improvement ofspending a night in London was added in time, and the plan became asperfect as plan could be.\n",
      "+ She is avery great favourite with some ladies of my acquaintance, Mrs. Hurst andMiss Bingley.\n",
      "+ It isdone, however, and it was done for the best.\n",
      "+ I have the greatest dislike in theworld to that sort of thing.\n",
      "+ Charlotte is anexcellent manager, I dare say.\n",
      "+ “His father was an excellent man,” said Mrs. Gardiner.\n",
      "+ “He is the best landlord, and the best master,” said she, “that everlived.\n",
      "+ “He is perfectly well-behaved, polite, and unassuming,” said her uncle.\n",
      "+ On reaching the house, they were shown through the hall into the saloon,whose northern aspect rendered it delightful for summer.\n",
      "+ Our distress, my dear Lizzy, is very great.\n",
      "+ And tell my dearLydia not to give any directions about her clothes till she has seen me,for she does not know which are the best warehouses.\n",
      "+ “My dear, dear Lydia!” she cried: “this is delightful indeed!\n",
      "+ Excellent parsonage-house!\n",
      "+ But, however, he is very welcome to come toNetherfield, if he likes it.\n",
      "+ Happy shall I be when his stay at Netherfield is over!”“I wish I could say anything to comfort you,” replied Elizabeth; “but itis wholly out of my power.\n",
      "+ If I could but see you as happy!\n",
      "+ He is perfectly amiable.\n",
      "+ Your idea of theponies is delightful.\n"
     ]
    }
   ],
   "source": [
    "print(\"The \" + str(len(positive_sentiment_sentences)) + \" most positive sentences:\")\n",
    "for sentence in positive_sentiment_sentences:\n",
    "    print(\"+ \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 14 most negative sentences:\n",
      "- Everybody is disgusted with his pride.\n",
      "- what canhave induced him to behave so cruelly?”“A thorough, determined dislike of me--a dislike which I cannot butattribute in some measure to jealousy.\n",
      "- To finda man agreeable whom one is determined to hate!\n",
      "- “I should like to know how he behaves among strangers.”“You shall hear, then--but prepare for something very dreadful.\n",
      "- The pause was to Elizabeth’s feelingsdreadful.\n",
      "- “Wickham sovery bad!\n",
      "- The separationbetween her and her family was rather noisy than pathetic.\n",
      "- It would be dreadful!\n",
      "- can she be ignorant of what youand Jane seem so well to understand?”“Oh, yes!--that, that is the worst of all.\n",
      "- “She is so fond of Mrs. Forster,” said she, “it will be quite shockingto send her away!\n",
      "- It was all     over before I arrived; so my curiosity was not so dreadfully racked     as _yours_ seems to have been.\n",
      "- He called it, therefore, his duty to step     forward, and endeavour to remedy an evil which had been brought on     by himself.\n",
      "- How you must have hated me after _that_ evening!”“Hate you!\n",
      "- You were disgusted with the women who were always speaking,and looking, and thinking for _your_ approbation alone.\n"
     ]
    }
   ],
   "source": [
    "print(\"The \" + str(len(negative_sentiment_sentences)) + \" most negative sentences:\")\n",
    "for sentence in negative_sentiment_sentences:\n",
    "    print(\"- \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is the poem text assignment for sentiment analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  }
 ],
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