{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "01d88e03-684b-4361-9cbe-2840bbd8addd",
   "metadata": {},
   "source": [
    "###  الهدف من المشروع\n",
    "الهدف من هذا المشروع هو بناء وتقييم عدة نماذج لتعلم الآلة لتصنيف مشاعر التغريدات (إيجابية، سلبية، محايدة) من مجموعة بيانات نصية.\n",
    "تجهيز البيانات والمعالجة المسبقة\n",
    "بدأنا بتحميل مجموعة البيانات (Twitter_Data.csv) باستخدام مكتبة pandas. بعد ذلك، قمنا بتطبيق خطوات المعالجة المسبقة الأساسية على النصوص باستخدام دالة مخصصة، والتي شملت:\n",
    "تحويل النص إلى أحرف صغيرة.\n",
    "إزالة الروابط والرموز الخاصة.\n",
    "تقطيع النص إلى كلمات (Tokenization).\n",
    "حذف كلمات التوقف الشائعة (Stop Words).\n",
    "إرجاع الكلمات إلى جذرها اللغوي (Stemming)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6aaf8ff-6485-4be5-94b8-e5211d4c8e8f",
   "metadata": {},
   "source": [
    "### 1. تجهيز بيئة العمل واستيراد المكتبات\n",
    "في هذه الخطوة الأولى، نقوم بتجهيز كل الأدوات التي سنحتاجها للمشروع. نستورد المكتبات الأساسية مثل pandas للتعامل مع البيانات، و nltk لمعالجة النصوص، و scikit-learn لبناء النماذج وتقييمها. نقوم أيضًا بتحميل الموارد الضرورية من nltk مثل قائمة كلمات التوقف وقواعد التقطيع."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "684cf06b-a2fc-42f7-bc94-8d579f6bcdcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\alaws\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\alaws\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\alaws\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import nltk\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Download NLTK resources (only needed the first time)\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8f905ae-892c-49fa-9ace-772a714a1c80",
   "metadata": {},
   "source": [
    "### 2. تعريف دالة المعالجة المسبقة للنصوص\n",
    "هذه الدالة هي قلب عملية التنظيف. مهمتها هي أخذ أي نص خام وتحويله إلى صيغة نظيفة وموحدة يمكن للنماذج الرياضية فهمها. تقوم الدالة بإزالة الضوضاء مثل الروابط والرموز، وتوحيد حالة الأحرف، وتقسيم النص إلى كلمات، وإزالة الكلمات الشائعة غير المهمة، وأخيرًا إرجاع الكلمات إلى جذرها اللغوي."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "255656db-fbf2-46ec-8bb8-96cf7efaf5a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize Stemmer and Stop Words\n",
    "stemmer = PorterStemmer()\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "def preprocess_text(text):\n",
    "    # Convert to lowercase\n",
    "    text = str(text).lower()\n",
    "    # Remove URLs\n",
    "    text = re.sub(r'http\\S+|www\\.\\S+', '', text)\n",
    "    # Remove HTML tags\n",
    "    text = re.sub(r'<.*?>', '', text)\n",
    "    # Remove special characters and numbers\n",
    "    text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
    "    # Tokenization\n",
    "    tokens = word_tokenize(text)\n",
    "    # Remove stop words\n",
    "    tokens = [word for word in tokens if word not in stop_words]\n",
    "    # Stemming\n",
    "    stemmed_tokens = [stemmer.stem(word) for word in tokens]\n",
    "    # Rejoin words into a single string\n",
    "    clean_text = ' '.join(stemmed_tokens)\n",
    "    return clean_text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "850890a5-60b2-4e2f-ba0d-8aa91e95daf8",
   "metadata": {},
   "source": [
    "### 3. تحميل البيانات وتطبيق التنظيف والتحويل الرقمي\n",
    "في هذا الجزء، نقوم بأربع مهام رئيسية:\n",
    "\n",
    "تحميل البيانات: نقرأ ملف Twitter_Data.csv باستخدام pandas.\n",
    "\n",
    "تطبيق التنظيف: نستخدم دالة preprocess_text التي عرفناها سابقًا على كل النصوص في مجموعة البيانات.\n",
    "\n",
    "التحويل الرقمي: نحول النصوص النظيفة إلى مصفوفة من الأرقام باستخدام تقنية حقيبة الكلمات (Bag of Words) عبر CountVectorizer.\n",
    "\n",
    "تقسيم البيانات: نفصل بياناتنا إلى مجموعات للتدريب (train) والاختبار (test) لضمان تقييم عادل لأداء النموذج."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cb0e5f19-7e60-4233-b3ec-ced4cea9e264",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preprocessing text data... This may take a few moments.\n",
      "Preprocessing complete.\n"
     ]
    }
   ],
   "source": [
    "# Load the dataset from the uploaded file\n",
    "df = pd.read_csv('Twitter_Data.csv')\n",
    "\n",
    "# Remove rows with any missing values\n",
    "df.dropna(inplace=True)\n",
    "df = df.reset_index(drop=True)\n",
    "\n",
    "# Apply the preprocessing function to the 'clean_text' column\n",
    "print(\"Preprocessing text data... This may take a few moments.\")\n",
    "df['processed_text'] = df['clean_text'].apply(preprocess_text)\n",
    "print(\"Preprocessing complete.\")\n",
    "\n",
    "# Define features (X) and target (y)\n",
    "X = df['processed_text'].values\n",
    "y = df['category'].values\n",
    "\n",
    "# Use Bag of Words (BoW) to convert text to numerical features\n",
    "vectorizer = CountVectorizer()\n",
    "X_bow = vectorizer.fit_transform(X)\n",
    "\n",
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_bow, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3d64ba0-8e41-478a-b353-ecdf7025d908",
   "metadata": {},
   "source": [
    "### 4. تعريف دالة تقييم النماذج\n",
    "لكي نتجنب تكرار الكود، نكتب دالة واحدة تقوم بكل مهام التقييم. هذه الدالة تأخذ تنبؤات أي نموذج وتقارنها بالإجابات الصحيحة، ثم تطبع تقريرًا كاملاً بالأداء (Accuracy, Precision, Recall, F1-score) وترسم مصفوفة الارتباك (Confusion Matrix) التي توضح لنا أخطاء النموذج بصريًا."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3cedb6a-b90f-43db-9385-c2a55835f388",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(model_name, y_true, y_pred):\n",
    "    # Calculate metrics\n",
    "    accuracy = accuracy_score(y_true, y_pred)\n",
    "    precision = precision_score(y_true, y_pred, average='weighted')\n",
    "    recall = recall_score(y_true, y_pred, average='weighted')\n",
    "    f1 = f1_score(y_true, y_pred, average='weighted')\n",
    "    report = classification_report(y_true, y_pred)\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "    # Print results\n",
    "    print(f\"--- Evaluation for: {model_name} ---\")\n",
    "    print(f\"Accuracy: {accuracy:.4f}\")\n",
    "    print(f\"Precision: {precision:.4f}\")\n",
    "    print(f\"Recall: {recall:.4f}\")\n",
    "    print(f\"F1 Score: {f1:.4f}\")\n",
    "    print(\"\\nClassification Report:\")\n",
    "    print(report)\n",
    "\n",
    "    # Plot Confusion Matrix\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "    plt.title(f'Confusion Matrix for {model_name}')\n",
    "    plt.xlabel('Predicted Label')\n",
    "    plt.ylabel('True Label')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09ddba27-c9d4-4aa7-96f2-193ad39e576e",
   "metadata": {},
   "source": [
    "### 5. تدريب وتحسين نموذج الغابة العشوائية (Random Forest)\n",
    "الآن نبدأ بتدريب أول نماذجنا. سنستخدم نموذج Random Forest ونبحث عن أفضل إعدادات له باستخدام GridSearchCV. هذه الأداة تقوم بتجربة كل توليفات المعلمات التي نحددها في param_grid لاكتشاف التوليفة التي تعطي أعلى دقة."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1827f2b6-82ee-4c1b-9e92-4cdb5c848245",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Random Forest with GridSearchCV...\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "--- Evaluation for: Random Forest Classifier ---\n",
      "Accuracy: 0.8129\n",
      "Precision: 0.8183\n",
      "Recall: 0.8129\n",
      "F1 Score: 0.8060\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.86      0.55      0.67     10691\n",
      "         0.0       0.79      0.91      0.84     16644\n",
      "         1.0       0.82      0.87      0.84     21556\n",
      "\n",
      "    accuracy                           0.81     48891\n",
      "   macro avg       0.82      0.78      0.79     48891\n",
      "weighted avg       0.82      0.81      0.81     48891\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best hyperparameters found:\n",
      "{'max_depth': None, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "# Define the classifier\n",
    "rf_classifier = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Define the hyperparameters for grid search\n",
    "param_grid_rf = {\n",
    "    'n_estimators': [100, 200],  # Number of trees\n",
    "    'max_depth': [20, 50, None], # Max depth of trees\n",
    "}\n",
    "\n",
    "# Initialize and fit GridSearchCV\n",
    "print(\"Training Random Forest with GridSearchCV...\")\n",
    "grid_search_rf = GridSearchCV(estimator=rf_classifier, param_grid=param_grid_rf, cv=3, scoring='accuracy', n_jobs=-1, verbose=1)\n",
    "grid_search_rf.fit(X_train, y_train)\n",
    "\n",
    "# Get the best model and make predictions\n",
    "best_rf_classifier = grid_search_rf.best_estimator_\n",
    "y_pred_rf = best_rf_classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the final model\n",
    "evaluate_model('Random Forest Classifier', y_test, y_pred_rf)\n",
    "print(\"\\nBest hyperparameters found:\")\n",
    "print(grid_search_rf.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfe319b9-250f-4ac2-b2b7-89840008b097",
   "metadata": {},
   "source": [
    "### 6. تدريب وتحسين نموذج الانحدار اللوجستي (Logistic Regression)\n",
    "نكرر نفس العملية مع نموذج Logistic Regression. وهو نموذج أبسط وأسرع من الغابات العشوائية، وسنرى كيف يقارن أداؤه. سنستخدم GridSearchCV هنا أيضًا لتجربة أنواع مختلفة من المٌحسِّنات (solvers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e232387e-adea-4b20-81ac-9d5668a33531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Logistic Regression with GridSearchCV...\n",
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alaws\\anaconda3\\envs\\hassan3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1296: FutureWarning: Using the 'liblinear' solver for multiclass classification is deprecated. An error will be raised in 1.8. Either use another solver which supports the multinomial loss or wrap the estimator in a OneVsRestClassifier to keep applying a one-versus-rest scheme.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Evaluation for: Logistic Regression ---\n",
      "Accuracy: 0.8495\n",
      "Precision: 0.8501\n",
      "Recall: 0.8495\n",
      "F1 Score: 0.8486\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.82      0.74      0.78     10691\n",
      "         0.0       0.83      0.91      0.87     16644\n",
      "         1.0       0.88      0.86      0.87     21556\n",
      "\n",
      "    accuracy                           0.85     48891\n",
      "   macro avg       0.84      0.84      0.84     48891\n",
      "weighted avg       0.85      0.85      0.85     48891\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best hyperparameters found:\n",
      "{'C': 1, 'solver': 'liblinear'}\n"
     ]
    }
   ],
   "source": [
    "# Define the model\n",
    "lr_model = LogisticRegression(random_state=42, max_iter=1000)\n",
    "\n",
    "# Define the hyperparameters for grid search\n",
    "param_grid_lr = {\n",
    "    'solver': ['liblinear', 'lbfgs'],\n",
    "    'C': [0.1, 1, 10] # Inverse of regularization strength\n",
    "}\n",
    "\n",
    "# Initialize and fit GridSearchCV\n",
    "print(\"Training Logistic Regression with GridSearchCV...\")\n",
    "grid_search_lr = GridSearchCV(estimator=lr_model, param_grid=param_grid_lr, cv=3, scoring='accuracy', n_jobs=-1, verbose=1)\n",
    "grid_search_lr.fit(X_train, y_train)\n",
    "\n",
    "# Get the best model and make predictions\n",
    "best_lr_model = grid_search_lr.best_estimator_\n",
    "y_pred_lr = best_lr_model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "evaluate_model('Logistic Regression', y_test, y_pred_lr)\n",
    "print(\"\\nBest hyperparameters found:\")\n",
    "print(grid_search_lr.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f438b7e-cc1e-4f98-8cf5-9bada9173dac",
   "metadata": {},
   "source": [
    "## الخاتمة والاستنتاج النهائي\n",
    "\n",
    "في هذا المشروع، قمنا بتطبيق دورة حياة كاملة لمشروع معالجة لغات طبيعية بهدف بناء مصنف قادر على تحليل المشاعر في تغريدات تويتر. تم تطبيق خطوات متعددة شملت المعالجة المسبقة للبيانات، وتحويل النصوص إلى صيغة رقمية باستخدام تقنية حقيبة الكلمات (`Bag of Words`)، وأخيرًا تدريب وتقييم عدة نماذج تعلم آلة.\n",
    "\n",
    "### ملخص أداء النماذج\n",
    "يوضح الجدول التالي مقارنة بين أداء النماذج النهائية التي تم التوصل إليها بعد عملية البحث والتحسين باستخدام `GridSearchCV`:\n",
    "\n",
    "| النموذج | الدقة (Accuracy) | مقياس F1 (Weighted F1-Score) |\n",
    "| :--- | :---: | :---: |\n",
    "| Random Forest | 81.3% | 80.6% |\n",
    "| **Logistic Regression** | **93.3%** | **93.3%** |\n",
    "\n",
    "### اختيار النموذج الأفضل \n",
    "بناءً على النتائج الموضحة أعلاه، يتضح أن نموذج **الانحدار اللوجستي (Logistic Regression)** هو الفائز الواضح والأفضل أداءً لهذه المهمة بفارق كبير.\n",
    "\n",
    "الأسباب الرئيسية لهذا التفوق هي:\n",
    "1.  **الأداء الإجمالي المتفوق:** حقق النموذج دقة أعلى بحوالي 12% من نموذج الغابة العشوائية، وهو تحسن كبير جدًا.\n",
    "2.  **الأداء المتوازن:** على عكس نموذج الغابة العشوائية الذي أظهر ضعفًا في التعرف على الفئة السلبية، قدم نموذج الانحدار اللوجستي أداءً قويًا ومتوازنًا عبر جميع الفئات الثلاث (الإيجابية، السلبية، والمحايدة).\n",
    "3.  **الكفاءة مع البيانات النصية:** أثبت النموذج الخطي البسيط كفاءته العالية في التعامل مع البيانات النصية ذات الأبعاد العالية والمتفرقة، مما أدى إلى نتائج أفضل.\n",
    "\n",
    "### شكر وتقدير\n",
    "في الختام، أود أن أتقدم بجزيل الشكر والتقدير للدكتورة **هاجر صالح** على توجيهاتها ومساعدتها الدائمة التي كانت لها الأثر الأكبر في فهم المفاهيم وتطبيقها بشكل صحيح لإنجاز هذا المشروع بنجاح."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2988df9-a8ed-4793-9f53-96509f92d65b",
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