Day 99.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the Libraries\n",
    "import nltk\n",
    "import urllib\n",
    "import bs4 as bs\n",
    "import re\n",
    "from gensim.models import Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gettings the data source\n",
    "source = urllib.request.urlopen('https://en.wikipedia.org/wiki/Global_warming').read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parsing the data/ creating BeautifulSoup object\n",
    "soup = bs.BeautifulSoup(source,'lxml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetching the data\n",
    "text = \"\"\n",
    "for paragraph in soup.find_all('p'):\n",
    "    text += paragraph.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing the data\n",
    "text = re.sub(r'\\[[0-9]*\\]',' ',text)\n",
    "text = re.sub(r'\\s+',' ',text)\n",
    "text = text.lower()\n",
    "text = re.sub(r'\\W',' ',text)\n",
    "text = re.sub(r'\\d',' ',text)\n",
    "text = re.sub(r'\\s+',' ',text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing the dataset\n",
    "sentences = nltk.sent_tokenize(text)\n",
    "\n",
    "sentences = [nltk.word_tokenize(sentence) for sentence in sentences]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training the Word2Vec model\n",
    "model = Word2Vec(sentences, min_count=1)\n",
    "\n",
    "words = model.wv.vocab"
   ]
  }
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