Sentiment Analysis for Moroccan Dialect.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "46AllxC7E2hK"
   },
   "source": [
    "# <font><center> **Sentiment Analysis for Moroccan Dialect** \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MA5607R9jU3a"
   },
   "source": [
    "### Reading and Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "04_YN444Jzcn"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "kg1IUmVByw_u",
    "outputId": "437e9cce-1fc0-4747-d331-dbb4be8ab012"
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   "outputs": [
    {
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       "\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>comment</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>العتماني عندو اللباقة و الجدال بالغة سد فمك</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>إن كتب لنا الموت بهذا الفيروس فذاك يبقى قضاء و...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>خوتي الله ينجيكم من هاد الفيروس الخبيث يا رب ا...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>الحمد لله على أن ڤيروس كورونا لا يعيش في ما فو...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>و بخصوص الكمامات فهو على صواب الكمامة تحمي فقط...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             comment  label\n",
       "0        العتماني عندو اللباقة و الجدال بالغة سد فمك      0\n",
       "1  إن كتب لنا الموت بهذا الفيروس فذاك يبقى قضاء و...      0\n",
       "2  خوتي الله ينجيكم من هاد الفيروس الخبيث يا رب ا...      0\n",
       "3  الحمد لله على أن ڤيروس كورونا لا يعيش في ما فو...      0\n",
       "4  و بخصوص الكمامات فهو على صواب الكمامة تحمي فقط...      0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import  numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "train = pd.read_excel(\"Final_Data.xlsx\" )\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "-WYy5ikAs7l3"
   },
   "outputs": [],
   "source": [
    "DATA_COLUMN = \"comment\"\n",
    "LABEL_COLUMN = \"label\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
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    "id": "beSl29tdrDYW",
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   "outputs": [
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     "data": {
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       "    }\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>comment</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>العتماني عندو اللباقة و الجدال بالغة سد فمك</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>إن كتب لنا الموت بهذا الفيروس فذاك يبقى قضاء و...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>خوتي الله ينجيكم من هاد الفيروس الخبيث يا رب ا...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>الحمد لله على أن ڤيروس كورونا لا يعيش في ما فو...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>و بخصوص الكمامات فهو على صواب الكمامة تحمي فقط...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4332</th>\n",
       "      <td>هاد الصداع كامل السبب ديالوا كروش الحرام اللي ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4333</th>\n",
       "      <td>هادشي بزاف ياك حبستونا شهرين بلا خدمة مرض كيتص...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4334</th>\n",
       "      <td>واحد التمساح عدنا ماعندوا نفس</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4335</th>\n",
       "      <td>وكلنا عليهم الله هاد المصانع غادي يخليونا فديو...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4336</th>\n",
       "      <td>واش هذا ديال دابا ؟؟؟؟؟؟</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4337 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                comment  label\n",
       "0           العتماني عندو اللباقة و الجدال بالغة سد فمك      0\n",
       "1     إن كتب لنا الموت بهذا الفيروس فذاك يبقى قضاء و...      0\n",
       "2     خوتي الله ينجيكم من هاد الفيروس الخبيث يا رب ا...      0\n",
       "3     الحمد لله على أن ڤيروس كورونا لا يعيش في ما فو...      0\n",
       "4     و بخصوص الكمامات فهو على صواب الكمامة تحمي فقط...      0\n",
       "...                                                 ...    ...\n",
       "4332  هاد الصداع كامل السبب ديالوا كروش الحرام اللي ...      1\n",
       "4333  هادشي بزاف ياك حبستونا شهرين بلا خدمة مرض كيتص...      1\n",
       "4334                      واحد التمساح عدنا ماعندوا نفس      1\n",
       "4335  وكلنا عليهم الله هاد المصانع غادي يخليونا فديو...      1\n",
       "4336                           واش هذا ديال دابا ؟؟؟؟؟؟      0\n",
       "\n",
       "[4337 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_MSAC = pd.DataFrame(train, columns=[\"comment\", LABEL_COLUMN])\n",
    "df_MSAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Asi hotman positive dima 😂'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_MSAC[\"comment\"][225]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aransia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aaransia import transliterate, SourceLanguageError\n",
    "for i in range(len(df_MSAC)):\n",
    "    row=df_MSAC.iloc[i][\"comment\"]\n",
    "    row=transliterate(row, source='ma', target='ar', universal=True)\n",
    "    df_MSAC.loc[:,\"comment\"][i]=row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'اسي هوتمان پوسيتيڤ ديما 😂'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_MSAC[\"comment\"][225]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "#Normalize_the_text:\n",
    "def normalize_arabic(text):\n",
    "    text = re.sub(\"[إأآا]\", \"ا\", text)\n",
    "    text = re.sub(\"ى\", \"ي\", text)\n",
    "    text = re.sub(\"ؤ\", \"ء\", text)\n",
    "    text = re.sub(\"ئ\", \"ء\", text)\n",
    "    text = re.sub(\"ة\", \"ه\", text)\n",
    "    text = re.sub(\"گ\", \"ك\", text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = [normalize_arabic(text) for text in df_MSAC['comment'] ]\n",
    "df_MSAC['comment']=sentences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Text cleaning will include several things:\n",
    "\n",
    "- removing missing values\n",
    "- removing punctuation \n",
    "- removing numbers \n",
    "- removing stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PpHbF5nIVz8L",
    "outputId": "b3252934-a68a-460d-ff00-438780392d4d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' الشفافيه و الثقه  لا بد ان تكون الثقه'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "df_MSAC['comment'] = df_MSAC['comment'].str.replace('\\d+', ' ')\n",
    "\n",
    "df_MSAC['comment'] = df_MSAC['comment'].str.replace('\\n', ' ')\n",
    "df_MSAC['comment'] = df_MSAC['comment'].str.replace('/', ' ')\n",
    "#removes punctuation\n",
    "df_MSAC['comment']= [re.sub(r'[^\\w\\s]','',s) for s in df_MSAC['comment']] \n",
    "#remove latin characters\n",
    "df_MSAC['comment']= [re.sub(r'[a-zA-Z ]',' ',s) for s in df_MSAC['comment']] \n",
    "df_MSAC['comment'][12]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "TLQq8BdQWiPQ"
   },
   "outputs": [],
   "source": [
    "##Common Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary libraries\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['إذ', 'إذا', 'إذما', 'إذن', 'أف', 'أقل', 'أكثر', 'ألا', 'إلا', 'التي', 'الذي', 'الذين', 'اللاتي', 'اللائي', 'اللتان', 'اللتيا', 'اللتين', 'اللذان', 'اللذين', 'اللواتي', 'إلى', 'إليك', 'إليكم', 'إليكما', 'إليكن', 'أم', 'أما', 'أما', 'إما', 'أن', 'إن', 'إنا', 'أنا', 'أنت', 'أنتم', 'أنتما', 'أنتن', 'إنما', 'إنه', 'أنى', 'أنى', 'آه', 'آها', 'أو', 'أولاء', 'أولئك', 'أوه', 'آي', 'أي', 'أيها', 'إي', 'أين', 'أين', 'أينما', 'إيه', 'بخ', 'بس', 'بعد', 'بعض', 'بك', 'بكم', 'بكم', 'بكما', 'بكن', 'بل', 'بلى', 'بما', 'بماذا', 'بمن', 'بنا', 'به', 'بها', 'بهم', 'بهما', 'بهن', 'بي', 'بين', 'بيد', 'تلك', 'تلكم', 'تلكما', 'ته', 'تي', 'تين', 'تينك', 'ثم', 'ثمة', 'حاشا', 'حبذا', 'حتى', 'حيث', 'حيثما', 'حين', 'خلا', 'دون', 'ذا', 'ذات', 'ذاك', 'ذان', 'ذانك', 'ذلك', 'ذلكم', 'ذلكما', 'ذلكن', 'ذه', 'ذو', 'ذوا', 'ذواتا', 'ذواتي', 'ذي', 'ذين', 'ذينك', 'ريث', 'سوف', 'سوى', 'شتان', 'عدا', 'عسى', 'عل', 'على', 'عليك', 'عليه', 'عما', 'عن', 'عند', 'غير', 'فإذا', 'فإن', 'فلا', 'فمن', 'في', 'فيم', 'فيما', 'فيه', 'فيها', 'قد', 'كأن', 'كأنما', 'كأي', 'كأين', 'كذا', 'كذلك', 'كل', 'كلا', 'كلاهما', 'كلتا', 'كلما', 'كليكما', 'كليهما', 'كم', 'كم', 'كما', 'كي', 'كيت', 'كيف', 'كيفما', 'لا', 'لاسيما', 'لدى', 'لست', 'لستم', 'لستما', 'لستن', 'لسن', 'لسنا', 'لعل', 'لك', 'لكم', 'لكما', 'لكن', 'لكنما', 'لكي', 'لكيلا', 'لم', 'لما', 'لن', 'لنا', 'له', 'لها', 'لهم', 'لهما', 'لهن', 'لو', 'لولا', 'لوما', 'لي', 'لئن', 'ليت', 'ليس', 'ليسا', 'ليست', 'ليستا', 'ليسوا', 'ما', 'ماذا', 'متى', 'مذ', 'مع', 'مما', 'ممن', 'من', 'منه', 'منها', 'منذ', 'مه', 'مهما', 'نحن', 'نحو', 'نعم', 'ها', 'هاتان', 'هاته', 'هاتي', 'هاتين', 'هاك', 'هاهنا', 'هذا', 'هذان', 'هذه', 'هذي', 'هذين', 'هكذا', 'هل', 'هلا', 'هم', 'هما', 'هن', 'هنا', 'هناك', 'هنالك', 'هو', 'هؤلاء', 'هي', 'هيا', 'هيت', 'هيهات', 'والذي', 'والذين', 'وإذ', 'وإذا', 'وإن', 'ولا', 'ولكن', 'ولو', 'وما', 'ومن', 'وهو', 'يا', 'أبٌ', 'أخٌ', 'حمٌ', 'فو', 'أنتِ', 'يناير', 'فبراير', 'مارس', 'أبريل', 'مايو', 'يونيو', 'يوليو', 'أغسطس', 'سبتمبر', 'أكتوبر', 'نوفمبر', 'ديسمبر', 'جانفي', 'فيفري', 'مارس', 'أفريل', 'ماي', 'جوان', 'جويلية', 'أوت', 'كانون', 'شباط', 'آذار', 'نيسان', 'أيار', 'حزيران', 'تموز', 'آب', 'أيلول', 'تشرين', 'دولار', 'دينار', 'ريال', 'درهم', 'ليرة', 'جنيه', 'قرش', 'مليم', 'فلس', 'هللة', 'سنتيم', 'يورو', 'ين', 'يوان', 'شيكل', 'واحد', 'اثنان', 'ثلاثة', 'أربعة', 'خمسة', 'ستة', 'سبعة', 'ثمانية', 'تسعة', 'عشرة', 'أحد', 'اثنا', 'اثني', 'إحدى', 'ثلاث', 'أربع', 'خمس', 'ست', 'سبع', 'ثماني', 'تسع', 'عشر', 'ثمان', 'سبت', 'أحد', 'اثنين', 'ثلاثاء', 'أربعاء', 'خميس', 'جمعة', 'أول', 'ثان', 'ثاني', 'ثالث', 'رابع', 'خامس', 'سادس', 'سابع', 'ثامن', 'تاسع', 'عاشر', 'حادي', 'أ', 'ب', 'ت', 'ث', 'ج', 'ح', 'خ', 'د', 'ذ', 'ر', 'ز', 'س', 'ش', 'ص', 'ض', 'ط', 'ظ', 'ع', 'غ', 'ف', 'ق', 'ك', 'ل', 'م', 'ن', 'ه', 'و', 'ي', 'ء', 'ى', 'آ', 'ؤ', 'ئ', 'أ', 'ة', 'ألف', 'باء', 'تاء', 'ثاء', 'جيم', 'حاء', 'خاء', 'دال', 'ذال', 'راء', 'زاي', 'سين', 'شين', 'صاد', 'ضاد', 'طاء', 'ظاء', 'عين', 'غين', 'فاء', 'قاف', 'كاف', 'لام', 'ميم', 'نون', 'هاء', 'واو', 'ياء', 'همزة', 'ي', 'نا', 'ك', 'كن', 'ه', 'إياه', 'إياها', 'إياهما', 'إياهم', 'إياهن', 'إياك', 'إياكما', 'إياكم', 'إياك', 'إياكن', 'إياي', 'إيانا', 'أولالك', 'تانِ', 'تانِك', 'تِه', 'تِي', 'تَيْنِ', 'ثمّ', 'ثمّة', 'ذانِ', 'ذِه', 'ذِي', 'ذَيْنِ', 'هَؤلاء', 'هَاتانِ', 'هَاتِه', 'هَاتِي', 'هَاتَيْنِ', 'هَذا', 'هَذانِ', 'هَذِه', 'هَذِي', 'هَذَيْنِ', 'الألى', 'الألاء', 'أل', 'أنّى', 'أيّ', 'ّأيّان', 'أنّى', 'أيّ', 'ّأيّان', 'ذيت', 'كأيّ', 'كأيّن', 'بضع', 'فلان', 'وا', 'آمينَ', 'آهِ', 'آهٍ', 'آهاً', 'أُفٍّ', 'أُفٍّ', 'أفٍّ', 'أمامك', 'أمامكَ', 'أوّهْ', 'إلَيْكَ', 'إلَيْكَ', 'إليكَ', 'إليكنّ', 'إيهٍ', 'بخٍ', 'بسّ', 'بَسْ', 'بطآن', 'بَلْهَ', 'حاي', 'حَذارِ', 'حيَّ', 'حيَّ', 'دونك', 'رويدك', 'سرعان', 'شتانَ', 'شَتَّانَ', 'صهْ', 'صهٍ', 'طاق', 'طَق', 'عَدَسْ', 'كِخ', 'مكانَك', 'مكانَك', 'مكانَك', 'مكانكم', 'مكانكما', 'مكانكنّ', 'نَخْ', 'هاكَ', 'هَجْ', 'هلم', 'هيّا', 'هَيْهات', 'وا', 'واهاً', 'وراءَك', 'وُشْكَانَ', 'وَيْ', 'يفعلان', 'تفعلان', 'يفعلون', 'تفعلون', 'تفعلين', 'اتخذ', 'ألفى', 'تخذ', 'ترك', 'تعلَّم', 'جعل', 'حجا', 'حبيب', 'خال', 'حسب', 'خال', 'درى', 'رأى', 'زعم', 'صبر', 'ظنَّ', 'عدَّ', 'علم', 'غادر', 'ذهب', 'وجد', 'ورد', 'وهب', 'أسكن', 'أطعم', 'أعطى', 'رزق', 'زود', 'سقى', 'كسا', 'أخبر', 'أرى', 'أعلم', 'أنبأ', 'حدَث', 'خبَّر', 'نبَّا', 'أفعل به', 'ما أفعله', 'بئس', 'ساء', 'طالما', 'قلما', 'لات', 'لكنَّ', 'ءَ', 'أجل', 'إذاً', 'أمّا', 'إمّا', 'إنَّ', 'أنًّ', 'أى', 'إى', 'أيا', 'ب', 'ثمَّ', 'جلل', 'جير', 'رُبَّ', 'س', 'علًّ', 'ف', 'كأنّ', 'كلَّا', 'كى', 'ل', 'لات', 'لعلَّ', 'لكنَّ', 'لكنَّ', 'م', 'نَّ', 'هلّا', 'وا', 'أل', 'إلّا', 'ت', 'ك', 'لمّا', 'ن', 'ه', 'و', 'ا', 'ي', 'تجاه', 'تلقاء', 'جميع', 'حسب', 'سبحان', 'شبه', 'لعمر', 'مثل', 'معاذ', 'أبو', 'أخو', 'حمو', 'فو', 'مئة', 'مئتان', 'ثلاثمئة', 'أربعمئة', 'خمسمئة', 'ستمئة', 'سبعمئة', 'ثمنمئة', 'تسعمئة', 'مائة', 'ثلاثمائة', 'أربعمائة', 'خمسمائة', 'ستمائة', 'سبعمائة', 'ثمانمئة', 'تسعمائة', 'عشرون', 'ثلاثون', 'اربعون', 'خمسون', 'ستون', 'سبعون', 'ثمانون', 'تسعون', 'عشرين', 'ثلاثين', 'اربعين', 'خمسين', 'ستين', 'سبعين', 'ثمانين', 'تسعين', 'بضع', 'نيف', 'أجمع', 'جميع', 'عامة', 'عين', 'نفس', 'لا سيما', 'أصلا', 'أهلا', 'أيضا', 'بؤسا', 'بعدا', 'بغتة', 'تعسا', 'حقا', 'حمدا', 'خلافا', 'خاصة', 'دواليك', 'سحقا', 'سرا', 'سمعا', 'صبرا', 'صدقا', 'صراحة', 'طرا', 'عجبا', 'عيانا', 'غالبا', 'فرادى', 'فضلا', 'قاطبة', 'كثيرا', 'لبيك', 'معاذ', 'أبدا', 'إزاء', 'أصلا', 'الآن', 'أمد', 'أمس', 'آنفا', 'آناء', 'أنّى', 'أول', 'أيّان', 'تارة', 'ثمّ', 'ثمّة', 'حقا', 'صباح', 'مساء', 'ضحوة', 'عوض', 'غدا', 'غداة', 'قطّ', 'كلّما', 'لدن', 'لمّا', 'مرّة', 'قبل', 'خلف', 'أمام', 'فوق', 'تحت', 'يمين', 'شمال', 'ارتدّ', 'استحال', 'أصبح', 'أضحى', 'آض', 'أمسى', 'انقلب', 'بات', 'تبدّل', 'تحوّل', 'حار', 'رجع', 'راح', 'صار', 'ظلّ', 'عاد', 'غدا', 'كان', 'ما انفك', 'ما برح', 'مادام', 'مازال', 'مافتئ', 'ابتدأ', 'أخذ', 'اخلولق', 'أقبل', 'انبرى', 'أنشأ', 'أوشك', 'جعل', 'حرى', 'شرع', 'طفق', 'علق', 'قام', 'كرب', 'كاد', 'هبّ']\n"
     ]
    }
   ],
   "source": [
    "print(stopwords.words('arabic'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' الشفافيه و الثقه  لا بد ان تكون الثقه'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_MSAC['comment'][12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "stopwords = list(set(nltk.corpus.stopwords.words('arabic')))\n",
    "df_MSAC[\"comment\"] = df_MSAC[\"comment\"].apply(lambda x: ' '.join([word for word in x.split() if word not in (stopwords)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tashaphyne.stemming import ArabicLightStemmer\n",
    "#Stemmer_LIGHT : Remove suffixes and affixes \n",
    "ArListem = ArabicLightStemmer()\n",
    "def stemmer_light(text):\n",
    "    text_words = []\n",
    "    words = text.split(\" \")\n",
    "    for c in words:\n",
    "        stem = ArListem.light_stem(c)\n",
    "        text_words.append(stem)\n",
    "    return ' '.join(text_words)\n",
    "\n",
    "#Root Stemming :  Transform the wrod into its root form\n",
    "def stemmer_root(text):\n",
    "    text_words = []\n",
    "    words = text.split(\" \")\n",
    "    for c in words:\n",
    "        stem = ArListem.light_stem(c)\n",
    "        text_words.append(stem)\n",
    "    return ' '.join(text_words)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = [stemmer_light(text) for text in df_MSAC['comment']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'شفافيه ثقه بد ان كو ثقه'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_MSAC['comment']=sentences\n",
    "df_MSAC['comment'][12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "jE90uEKEWN7q"
   },
   "outputs": [],
   "source": [
    "#we need to create a document term matrix using CountVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "def get_top_n_words(corpus, n=None):\n",
    "\n",
    "    vec = CountVectorizer().fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in  vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AZn_B25iWhSL",
    "outputId": "2f2aa4dc-a5bd-4c2e-a2fb-6a527734af9c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('له', 1358),\n",
      " ('عل', 1220),\n",
      " ('ان', 913),\n",
      " ('حال', 791),\n",
      " ('لا', 646),\n",
      " ('عد', 574),\n",
      " ('را', 571),\n",
      " ('ناس', 569),\n",
      " ('هاد', 501),\n",
      " ('اش', 473),\n",
      " ('حمد', 450),\n",
      " ('مغرب', 428),\n",
      " ('ديال', 398),\n",
      " ('صح', 381),\n",
      " ('حجر', 381),\n",
      " ('حت', 377),\n",
      " ('سب', 338),\n",
      " ('نا', 332),\n",
      " ('على', 320),\n",
      " ('مرض', 296)]\n"
     ]
    }
   ],
   "source": [
    "#We inspect the top word in a sample from our corpus without stemming the words\n",
    "from pprint import pprint\n",
    "topWords=get_top_n_words(df_MSAC['comment'],n=20)\n",
    "pprint(topWords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wfroMFLnX43j"
   },
   "source": [
    "### Word Cloud\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MRw40_21XxwM",
    "outputId": "fd288571-a232-42dd-8d4f-776e50b51d5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000023D1C2AC100>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/bidi/\n",
      "WARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000023D1C1AAA90>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/bidi/\n",
      "WARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000023D1C1AA7F0>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/bidi/\n",
      "WARNING: Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000023D1B48FA30>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/bidi/\n",
      "WARNING: Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000023D1C1ED1F0>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/bidi/\n",
      "ERROR: Could not find a version that satisfies the requirement bidi (from versions: none)\n",
      "ERROR: No matching distribution found for bidi\n"
     ]
    }
   ],
   "source": [
    "pip install bidi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 198
    },
    "id": "zXHbTEwuYBCD",
    "outputId": "6ba3872f-4658-495f-9615-427f1f050961"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nltk\n",
    "#nltk.download('stopwords')\n",
    "from bidi.algorithm import get_display\n",
    "import matplotlib.pyplot as plt\n",
    "import arabic_reshaper\n",
    "import os\n",
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "#stopwords= pd.read_excel(\"stop_words_Arabic.xlsx\")\n",
    "arab_stopwords = list(set(nltk.corpus.stopwords.words(\"arabic\")))\n",
    "sample_corpus=' '.join(df_MSAC['comment'])\n",
    "data = arabic_reshaper.reshape(sample_corpus)\n",
    "data = get_display(data) # add this  \n",
    "wordcloud = WordCloud(font_path = 'arial.ttf',stopwords = arab_stopwords, background_color = \"white\", max_font_size = 50, max_words = 100).generate(data)\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UffTSw3RZOm9"
   },
   "source": [
    "Then we remove Arabic Stopwords and regenerate wordcloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MQ_eW3jHHG02"
   },
   "source": [
    "## Sentiment Analysis classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TVay9KamnC3I"
   },
   "source": [
    "### Creating training dataset for the classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "hr84ozGinCFh"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "from sklearn.model_selection import train_test_split\n",
    "all_datasets= []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yzZiHrnzqmIQ",
    "outputId": "c36445ff-349c-462e-b5dc-1433dc9de574"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    2899\n",
      "0    1438\n",
      "Name: label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_MSAC[LABEL_COLUMN].value_counts())\n",
    "label_list_MSAC = list(df_MSAC[LABEL_COLUMN].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DecisionTree with CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f1r0QaWfsXh5",
    "outputId": "1bbc5ddb-ec21-4688-a8d4-6b1fb7e8a521"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score (=accuracy) du modèle decision_tree avec CountVectorizer sur le jeu de test :  74.42 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Modèle 1\n",
    "df_MSAC.columns = [DATA_COLUMN, LABEL_COLUMN]\n",
    "\n",
    "#train_MSAC, test_MSAC = train_test_split(df_MSAC, test_size=0.2, random_state=42)\n",
    "\n",
    "# Vectorisation des critiques\n",
    "vectorizer = CountVectorizer(max_features=256)\n",
    "X = vectorizer.fit_transform(df_MSAC[DATA_COLUMN])\n",
    "y = df_MSAC[LABEL_COLUMN]\n",
    "\n",
    "# Séparation des données en 2 parties : entraînement / Test\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y, test_size=0.2,random_state=42)\n",
    "# Entraînement du modèle\n",
    "decision_tree = DecisionTreeClassifier(random_state=0)\n",
    "decision_tree = decision_tree.fit(train_X, train_y)\n",
    "predictions = decision_tree.predict(val_X)\n",
    "print('Score (=accuracy) du modèle decision_tree avec CountVectorizer sur le jeu de test : ', round(accuracy_score(val_y, predictions) * 100, 2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7msG_iQwvDQZ",
    "outputId": "ce6a88d9-7784-46a7-a3b8-09f273c446b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Il y a 11031 mots différents dans notre corpus.\n"
     ]
    }
   ],
   "source": [
    "vectorizer = CountVectorizer()\n",
    "vectorizer.fit(df_MSAC[DATA_COLUMN])\n",
    "vocab_size = len(vectorizer.vocabulary_)\n",
    "print(f'Il y a {vocab_size} mots différents dans notre corpus.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DecisionTree with TfidfVectorizer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gxItNpCytRzw",
    "outputId": "7b4d6c54-8d7d-4b94-e50c-b0b3709f2c72"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score (=accuracy) du modèle decision_tree avec TfidfVectorizer sur le jeu de test :  78.0 %\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import nltk\n",
    "import random\n",
    "# Train the classifier\n",
    "vectorizer = TfidfVectorizer(max_features=2000)\n",
    "X = vectorizer.fit_transform(df_MSAC[DATA_COLUMN])\n",
    "y = df_MSAC[LABEL_COLUMN]\n",
    "\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y,  test_size=0.2,random_state=42)\n",
    "decision_tree = DecisionTreeClassifier(random_state=0)\n",
    "decision_tree = decision_tree.fit(train_X, train_y)\n",
    "predictions = decision_tree.predict(val_X)\n",
    "print('Score (=accuracy) du modèle decision_tree avec TfidfVectorizer sur le jeu de test : ', round(accuracy_score(predictions,val_y) * 100, 2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score (=accuracy) du modèle svm  sur le jeu de test :  84.33 %\n"
     ]
    }
   ],
   "source": [
    "#modéle SVM\n",
    "from sklearn import svm\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y,  test_size=0.2,random_state=42)\n",
    "lin_clf = svm.LinearSVC()\n",
    "lin_cl=lin_clf.fit(train_X, train_y)\n",
    "predictions = lin_cl.predict(val_X)\n",
    "print('Score (=accuracy) du modèle svm  sur le jeu de test : ', round(accuracy_score(predictions,val_y) * 100, 2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive bayes with CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score (=accuracy) du modèle  sur le jeu de test :  83.29 %\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "import random\n",
    "from sklearn.naive_bayes import  MultinomialNB\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Train the classifier\n",
    "vectorizer = CountVectorizer(max_features=2000)\n",
    "X = vectorizer.fit_transform(df_MSAC[DATA_COLUMN])\n",
    "y = df_MSAC[LABEL_COLUMN]\n",
    "\n",
    "# Séparation des données en 2 parties : entraînement / test\n",
    "train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.2,random_state=42)\n",
    "# Entraînement du modèle\n",
    "mnb = MultinomialNB()\n",
    "mnb.fit(train_X, train_y)\n",
    "\n",
    "# Test the classifier\n",
    "predictions = pd.Series(mnb.predict(test_X))\n",
    "print('Score (=accuracy) du modèle  sur le jeu de test : ', round(accuracy_score(test_y, predictions) * 100, 2), '%')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive bayes with TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6fJwM8CHv4gj",
    "outputId": "c5c548b8-da4b-4ffa-9fb4-d1453c5e9a2c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score (=accuracy) du modèle  sur le jeu de test :  83.99 %\n"
     ]
    }
   ],
   "source": [
    "# Modèle3 naive_bayes\n",
    "import nltk\n",
    "import random\n",
    "from sklearn.naive_bayes import BernoulliNB, MultinomialNB, GaussianNB\n",
    "\n",
    "# Train the classifier\n",
    "vectorizer = TfidfVectorizer(max_features=2000)\n",
    "X = vectorizer.fit_transform(df_MSAC[DATA_COLUMN])\n",
    "y = df_MSAC[LABEL_COLUMN]\n",
    "\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y,  test_size=0.2,random_state=42)\n",
    "mnb = MultinomialNB()\n",
    "mnb.fit(train_X, train_y)\n",
    "\n",
    "# Test the classifier\n",
    "predictions = pd.Series(mnb.predict(val_X))\n",
    "print('Score (=accuracy) du modèle  sur le jeu de test : ', round(accuracy_score(val_y, predictions) * 100, 2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score (=accuracy) du modèle decision_tree avec TfidfVectorizer sur le jeu de test :  63.82 %\n"
     ]
    }
   ],
   "source": [
    "#modèle random forest \n",
    "# Import the model we are using\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# Instantiate model with 1000 decision trees\n",
    "rf = RandomForestClassifier(max_depth=2, random_state = 42)\n",
    "# Train the model on training data\n",
    "randomforest=rf.fit(train_X, train_y);\n",
    "predictions = randomforest.predict(val_X)\n",
    "print('Score (=accuracy) du modèle decision_tree avec TfidfVectorizer sur le jeu de test : ', round(accuracy_score(predictions,val_y) * 100, 2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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