examples/text/ArabicHotelReviews-nbsvm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\";\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"; "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using Keras version: 2.2.4\n"
     ]
    }
   ],
   "source": [
    "import ktrain\n",
    "from ktrain import text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building an Arabic Sentiment Analyzer\n",
    "\n",
    "In this notebook, we will build a simple, fast, and accurate Arabic-language text classification model in 4 simple steps. More specifically, we will build a model that classifies Arabic hotel reviews as either positive or negative.\n",
    "\n",
    "The dataset can be downloaded from Ashraf Elnagar's GitHub repository (https://github.com/elnagara/HARD-Arabic-Dataset).\n",
    "\n",
    "Each entry in the dataset includes a review in Arabic and a rating between 1 and 5.  We will convert this to a binary classification dataset by assigning reviews with a rating of above 3 a positive label of 1 and assigning reviews with a rating of less than 3 a negative label of 0.\n",
    "\n",
    "(**Disclaimer:** I don't speak Arabic. Please forgive mistakes.) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>text</th>\n",
       "      <th>neg</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>“ممتاز”. النظافة والطاقم متعاون.</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>استثنائي. سهولة إنهاء المعاملة في الاستقبال. ل...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>استثنائي. انصح بأختيار الاسويت و بالاخص غرفه ر...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>“استغرب تقييم الفندق كخمس نجوم”. لا شي. يستحق ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>جيد. المكان جميل وهاديء. كل شي جيد ونظيف بس كا...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  neg  pos\n",
       "0                  “ممتاز”. النظافة والطاقم متعاون.     1    0\n",
       "1  استثنائي. سهولة إنهاء المعاملة في الاستقبال. ل...    0    1\n",
       "2  استثنائي. انصح بأختيار الاسويت و بالاخص غرفه ر...    0    1\n",
       "3  “استغرب تقييم الفندق كخمس نجوم”. لا شي. يستحق ...    1    0\n",
       "4  جيد. المكان جميل وهاديء. كل شي جيد ونظيف بس كا...    0    1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert ratings to a binary format:  1=positive, 0=negative\n",
    "import pandas as pd\n",
    "df = pd.read_csv('data/arabic_hotel_reviews/balanced-reviews.txt', delimiter='\\t', encoding='utf-16')\n",
    "df = df[['rating', 'review']] \n",
    "df['rating'] = df['rating'].apply(lambda x: 'neg' if x < 3 else 'pos')\n",
    "df.columns = ['label', 'text']\n",
    "df = pd.concat([df, df.label.astype('str').str.get_dummies()], axis=1, sort=False)\n",
    "df = df[['text', 'neg', 'pos']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP 1:  Load and Preprocess the Data\n",
    "\n",
    "First, we use the `texts_from_df` function to load and preprocess the data in to arrays that can be directly fed into a neural network model. \n",
    "\n",
    "We set `val_pct` as 0.1, which will automatically sample 10% of the data for validation.  We specifiy `preprocess_mode='standard'` to employ normal text preprocessing.  If you are using the BERT model (i.e., 'bert'), you should use `preprocess_mode='bert'`.\n",
    "\n",
    "**Notice that there is nothing speical or extra we need to do here for non-English text.**  *ktrain* automatically detects the language and character encoding and prepares the data and configures the model appropriately.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "language: ar\n",
      "Word Counts: 151716\n",
      "Nrows: 95128\n",
      "95128 train sequences\n",
      "Average train sequence length: 23\n",
      "Adding 3-gram features\n",
      "max_features changed to 2689751 with addition of ngrams\n",
      "Average train sequence length with ngrams: 66\n",
      "x_train shape: (95128,75)\n",
      "y_train shape: (95128,2)\n",
      "10570 test sequences\n",
      "Average test sequence length: 22\n",
      "Average test sequence length with ngrams: 41\n",
      "x_test shape: (10570,75)\n",
      "y_test shape: (10570,2)\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test), preproc = text.texts_from_df(df, \n",
    "                                                                   'text', # name of column containing review text\n",
    "                                                                   label_columns=['neg', 'pos'],\n",
    "                                                                   maxlen=75, \n",
    "                                                                   max_features=100000,\n",
    "                                                                   preprocess_mode='standard',\n",
    "                                                                   val_pct=0.1,\n",
    "                                                                   ngram_range=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP 2:  Create a Model and Wrap in Learner Object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will employ a neural implementation of the [NBSVM](https://www.aclweb.org/anthology/P12-2018/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 75\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-10000\n",
      "rows: 10001-20000\n",
      "rows: 20001-30000\n",
      "rows: 30001-40000\n",
      "rows: 40001-50000\n",
      "rows: 50001-60000\n",
      "rows: 60001-70000\n",
      "rows: 70001-80000\n",
      "rows: 80001-90000\n",
      "rows: 90001-95128\n",
      "computing log-count ratios...\n",
      "done.\n"
     ]
    }
   ],
   "source": [
    "model = text.text_classifier('nbsvm', (x_train, y_train) , preproc=preproc)\n",
    "learner = ktrain.get_learner(model, \n",
    "                             train_data=(x_train, y_train), \n",
    "                             val_data=(x_test, y_test), \n",
    "                             batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP 3: Estimate the LR\n",
    "We'll use the *ktrain* learning rate finder to find a good learning rate to use with *nbsvm*.  We will, then, select the highest learning rate associated with a still falling loss.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulating training for different learning rates... this may take a few moments...\n",
      "Epoch 1/1024\n",
      "48736/95128 [==============>...............] - ETA: 8s - loss: 0.5553 - acc: 0.7843\n",
      "\n",
      "done.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learner.lr_find(show_plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP 4: Train the Model\n",
    "\n",
    "We will use the `autofit` method that employs a triangular learning rate policy and train 1 epoch.\n",
    "\n",
    "As shown in the cell below, our final validation accuracy is **94%** with only ~20 seconds of training over a single epoch!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "begin training using triangular learning rate policy with max lr of 0.005...\n",
      "Train on 95128 samples, validate on 10570 samples\n",
      "Epoch 1/1\n",
      "95128/95128 [==============================] - 21s 219us/step - loss: 0.2227 - acc: 0.9343 - val_loss: 0.1754 - val_acc: 0.9427\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f58518c9320>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learner.autofit(5e-3, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspecting Misclassifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "id:5338 | loss:13.94 | true:neg | pred:pos)\n",
      "\n",
      "“ممتاز جدا وأشكر كل العاملين في الفندق ع حسن ممتاز جدا وانصح به من جميع النواحي نظافه وتعامل راقي ومواصلات الي الحرم ه مدار الساعه وإفطار ممتاز وأشكر موظفو الاستقبال ع حسن اسقبالهم مكان مميز وسعر مميز فقطالماء للتعريف\n"
     ]
    }
   ],
   "source": [
    "learner.view_top_losses(n=1, preproc=preproc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Google Translate, the above roughly translates to:\n",
    "> *The hotel is very clean and the staff are very friendly and helpful.*\n",
    "\n",
    "This is clearly a positive review and was predicted as positive by our model.  The \"ground truth\" rating for this review is incorrect and explains the high loss. Perhaps the customer accidently entered a low rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Predictions on New Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = ktrain.get_predictor(learner.model, preproc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting label for the text\n",
    "> \"*The room was clean, the food excellent, and I loved the view from my room.*\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pos'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.predict(\"الغرفة كانت نظيفة ، الطعام ممتاز ، وأنا أحب المنظر من غرفتي.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting label for:\n",
    "> \"*This hotel was too expensive and the staff is rude.*\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'neg'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.predict('كان هذا الفندق باهظ الثمن والموظفين غير مهذبين.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save our Predictor for Later Deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model for later use\n",
    "p.save('/tmp/arabic_predictor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reload from disk\n",
    "p = ktrain.load_predictor('/tmp/arabic_predictor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pos'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# still works as expected after reloading from disk\n",
    "p.predict(\"الغرفة كانت نظيفة ، الطعام ممتاز ، وأنا أحب المنظر من غرفتي.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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