NLP - bag of words and n-gram.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NLP bag-of-words and n-gram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analisys of data: movie review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>target</th>\n",
       "      <th>reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neg</td>\n",
       "      <td>plot : two teen couples go to a church party ,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neg</td>\n",
       "      <td>the happy bastard's quick movie review \\ndamn ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>neg</td>\n",
       "      <td>it is movies like these that make a jaded movi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>neg</td>\n",
       "      <td>\" quest for camelot \" is warner bros . ' firs...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>neg</td>\n",
       "      <td>synopsis : a mentally unstable man undergoing ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>neg</td>\n",
       "      <td>capsule : in 2176 on the planet mars police ta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>neg</td>\n",
       "      <td>so ask yourself what \" 8mm \" ( \" eight millime...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>neg</td>\n",
       "      <td>that's exactly how long the movie felt to me ....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>neg</td>\n",
       "      <td>call it a road trip for the walking wounded . ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>neg</td>\n",
       "      <td>plot : a young french boy sees his parents kil...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  target                                            reviews\n",
       "0    neg  plot : two teen couples go to a church party ,...\n",
       "1    neg  the happy bastard's quick movie review \\ndamn ...\n",
       "2    neg  it is movies like these that make a jaded movi...\n",
       "3    neg   \" quest for camelot \" is warner bros . ' firs...\n",
       "4    neg  synopsis : a mentally unstable man undergoing ...\n",
       "5    neg  capsule : in 2176 on the planet mars police ta...\n",
       "6    neg  so ask yourself what \" 8mm \" ( \" eight millime...\n",
       "7    neg  that's exactly how long the movie felt to me ....\n",
       "8    neg  call it a road trip for the walking wounded . ...\n",
       "9    neg  plot : a young french boy sees his parents kil..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/reviews.csv\")\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_punctuation(text):\n",
    "    import string \n",
    "    for punctuation in string.punctuation:\n",
    "        text = text.replace(punctuation, '')\n",
    "    return text\n",
    "def lower_text(text):\n",
    "    return text.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['reviews'] = remove_punctuation(data['reviews'])\n",
    "data['reviews'] = lower_text(data['reviews'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### bag-of-words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vectorizer\n",
    "vectorizer = CountVectorizer()\n",
    "\n",
    "X = vectorizer.fit_transform(data['reviews'])\n",
    "y = data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8145"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cros validate and Naive Bayes Model\n",
    "nb_model = MultinomialNB()\n",
    "\n",
    "cv_results = cross_validate(nb_model, X, y, cv=5)\n",
    "\n",
    "cv_results['test_score'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## N-gram (2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.843"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(ngram_range=(2,2))\n",
    "\n",
    "X = vectorizer.fit_transform(data['reviews'])\n",
    "\n",
    "cv_results = cross_validate(nb_model, X, y, cv=5)\n",
    "\n",
    "cv_results['test_score'].mean()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}