NLTK.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Installing NLTK and Downloading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting nltk==3.3\n",
      "  Downloading nltk-3.3.0.zip (1.4 MB)\n",
      "Requirement already satisfied: six in c:\\users\\msi\\anaconda3\\lib\\site-packages (from nltk==3.3) (1.15.0)\n",
      "Building wheels for collected packages: nltk\n",
      "  Building wheel for nltk (setup.py): started\n",
      "  Building wheel for nltk (setup.py): finished with status 'done'\n",
      "  Created wheel for nltk: filename=nltk-3.3-py3-none-any.whl size=1394475 sha256=f929f163361be55271a57d0f9597732595e13706af630c0b4feaef989c35a0f8\n",
      "  Stored in directory: c:\\users\\msi\\appdata\\local\\pip\\cache\\wheels\\19\\1d\\3a\\0a8c14c30132b4f9ffd796efbb6746f15b3d6bcfc1055a9346\n",
      "Successfully built nltk\n",
      "Installing collected packages: nltk\n",
      "  Attempting uninstall: nltk\n",
      "    Found existing installation: nltk 3.5\n",
      "    Uninstalling nltk-3.5:\n",
      "      Successfully uninstalled nltk-3.5\n",
      "Successfully installed nltk-3.3\n"
     ]
    }
   ],
   "source": [
    "!pip install nltk==3.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package twitter_samples to\n",
      "[nltk_data]     C:\\Users\\msi\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping corpora\\twitter_samples.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download(\"twitter_samples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Tokenizing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import twitter_samples\n",
    "positive_tweets=twitter_samples.strings('positive_tweets.json')\n",
    "negative=twitter_samples.strings('negative_tweets.json')\n",
    "text=twitter_samples.strings('tweets.20150430-223406.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\msi\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers\\punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n",
      "['#FollowFriday', '@France_Inte', '@PKuchly57', '@Milipol_Paris', 'for', 'being', 'top', 'engaged', 'members', 'in', 'my', 'community', 'this', 'week', ':)']\n"
     ]
    }
   ],
   "source": [
    "tweet_tokens=twitter_samples.tokenized('positive_tweets.json')[0]\n",
    "print(len(tweet_tokens))\n",
    "print(tweet_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet_tokens=twitter_samples.tokenized('positive_tweets.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Normalizing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\msi\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\msi\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping taggers\\averaged_perceptron_tagger.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('wordnet')\n",
    "nltk.download('averaged_perceptron_tagger')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# main link\n",
    "https://www.digitalocean.com/community/tutorials/how-to-perform-sentiment-analysis-in-python-3-using-the-natural-language-toolkit-nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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