Project1_useModel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project 1 for LING413 \n",
    "##### Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import codecs\n",
    "import pandas as pd\n",
    "import random\n",
    "from sklearn import feature_extraction\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load and Format Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                              Review  Rating\n",
      "0  nice hotel expensive parking got good deal sta...       4\n",
      "1  ok nothing special charge diamond member hilto...       2\n",
      "2  nice rooms not 4* experience hotel monaco seat...       3\n",
      "3  unique, great stay, wonderful time hotel monac...       5\n",
      "4  great stay great stay, went seahawk game aweso...       5\n",
      "(20491, 2)\n"
     ]
    }
   ],
   "source": [
    "file = \"tripadvisor_hotel_reviews.csv\"\n",
    "\n",
    "data_df = pd.read_csv(file, sep = \",\")\n",
    "\n",
    "print(data_df.head())\n",
    "print(data_df.shape)\n",
    "\n",
    "features = feature_extraction.text.CountVectorizer(input='content', \n",
    "                                                encoding='utf-8',\n",
    "                                                lowercase=True, \n",
    "                                                tokenizer = None,\n",
    "                                                decode_error='ignore',\n",
    "                                                ngram_range=(3, 3), \n",
    "                                                analyzer='word',\n",
    "                                                max_features=10000, #This sets vocab size\n",
    "                                                )\n",
    "\n",
    "data_x = features.fit_transform(data_df['Review'].values.astype('U'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file1 = open(\"trigram_tweet_classification_model.p\", \"rb\")\n",
    "\n",
    "cls = pickle.load(file1)\n",
    "\n",
    "file1.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use Model on New Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = cls.predict(data_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Append Predictions to New Data and Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df.insert(0, 'country', preds)\n",
    "data_df.to_csv('trigram_tripadvisor.csv', index=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}