kaggle-summary-quora-insincere-questions-classification.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install --user --upgrade pip \n",
    "!pip3 install --user nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "nltk.download('wordnet')\n",
    "nltk.download('punkt')\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "nltk.download('gutenberg')\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.tokenize import sent_tokenize \n",
    "\n",
    "corpus = '''Tokenization is the process of tokenizing or splitting a string, text into a list of tokens. One can think of token as parts like a word is a token in a sentence, and a sentence is a token in a paragraph.'''\n",
    "\n",
    "print(sent_tokenize(corpus))\n",
    "print(word_tokenize(corpus))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "corpus = ['rocks', 'gone', 'better']\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "print([lemmatizer.lemmatize(w) for w in corpus])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk import pos_tag\n",
    "\n",
    "def lemmatize_sent(text): \n",
    "    pos_dict = {'NN':'n', 'JJ':'a', 'VB':'v', 'RB':'r'}\n",
    "    word_list = []\n",
    "    for word, tag in pos_tag(word_tokenize(text)):\n",
    "        pos = pos_dict[tag[0:2]] if tag[0:2] in pos_dict else 'n'\n",
    "        word_list.append(lemmatizer.lemmatize(word, pos=pos))\n",
    "    return word_list\n",
    "\n",
    "sentence = 'He is walking to school'\n",
    "print('lemmatize word by word: ', [lemmatizer.lemmatize(w) for w in word_tokenize(sentence)])\n",
    "print('lemmatize with context: ', lemmatize_sent(sentence))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import PorterStemmer\n",
    "\n",
    "corpus = ['rocks', 'going', 'history']\n",
    "stemmer = PorterStemmer()\n",
    "print([stemmer.stem(w) for w in corpus])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import gutenberg\n",
    "import time\n",
    " \n",
    "def timing(func):\n",
    "    def decorate(*args, **kwargs):\n",
    "        start = time.time()\n",
    "        func(*args, **kwargs)\n",
    "        print(\"%-30s: %-7.2f ms\" % (func.__name__, (time.time() - start) * 1000))\n",
    "    return decorate\n",
    "\n",
    "@timing\n",
    "def stemming(text):\n",
    "    [stemmer.stem(w) for w in word_tokenize(sentence)]\n",
    "\n",
    "@timing\n",
    "def lemmatize(text):\n",
    "    lemmatize_sent(text)\n",
    "\n",
    "@timing\n",
    "def lemmatize_without_context(text):  \n",
    "    [lemmatizer.lemmatize(w) for w in word_tokenize(sentence)]\n",
    "\n",
    "book = gutenberg.raw(\"austen-sense.txt\")\n",
    "\n",
    "stemming(book)\n",
    "lemmatize(book)\n",
    "lemmatize_without_context(book)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "\n",
    "corpus = ['I', 'am', 'a', 'boy']\n",
    "print([w for w in corpus if w not in set(stopwords.words('english'))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install --user scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "vectorizer = CountVectorizer()\n",
    "corpus = [\n",
    "    'He is a teacher',\n",
    "    'I am student',\n",
    "    'She is also a student',\n",
    "]\n",
    "X = vectorizer.fit_transform(corpus)\n",
    "\n",
    "print(vectorizer.get_feature_names())\n",
    "print(list(vectorizer.stop_words_))\n",
    "print(X.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "vectorizer = TfidfVectorizer()\n",
    "corpus = [\n",
    "    'He is a teacher',\n",
    "    'I am student',\n",
    "    'She is also a student',\n",
    "]\n",
    "X = vectorizer.fit_transform(corpus)\n",
    "\n",
    "print(vectorizer.get_feature_names())\n",
    "print(X.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install --user numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "#The vector calculation of the sentence He is a teacher.\n",
    "tfidf_he = 1/3 * (np.log((3+1)/(1+1))+1)\n",
    "tfidf_is = 1/3 * (np.log((3+1)/(2+1))+1)\n",
    "tfidf_teacher = 1/3 * (np.log((3+1)/(1+1))+1)\n",
    "\n",
    "print(np.divide([tfidf_he, tfidf_is, tfidf_teacher], np.sqrt(tfidf_he*tfidf_he + tfidf_is*tfidf_is + tfidf_teacher* tfidf_teacher)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install --user gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim.downloader\n",
    "from gensim.models import Word2Vec\n",
    "\n",
    "word2vec = gensim.downloader.load('word2vec-google-news-300')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(word2vec.most_similar('car'))\n",
    "print(word2vec.word_vec('car'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install tensorflow"
   ]
  }
 ]
}