{"id":31164242,"url":"https://github.com/gehad-ahmed30/natural-language-processing","last_synced_at":"2025-10-05T23:27:22.318Z","repository":{"id":301758783,"uuid":"1010229636","full_name":"gehad-Ahmed30/Natural-Language-Processing","owner":"gehad-Ahmed30","description":"This repository showcases a collection of practical NLP projects, ranging from sentiment analysis to spam detection. 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The goal is to classify whether a review is **positive (Liked = 1)** or **negative (Liked = 0)**.\n\n---\n\n## 🧠 Project Workflow\n\n1. **Importing Data \u0026 Libraries**\n2. **Text Preprocessing (NLTK):**\n   - Lowercasing\n   - Removing punctuation\n   - Removing stopwords\n   - Stemming (Porter Stemmer)\n3. **Vectorization:**\n   - Using `CountVectorizer`\n4. **Model Building:**\n   - Multinomial Naive Bayes Classifier\n5. **Model Evaluation:**\n   - Accuracy, Confusion Matrix, Classification Report\n6. **Model Saving:**\n   - Exported using `joblib`\n\n---\n\n## 📊 Dataset\n\n- Source: [Kaggle – Restaurant Reviews (TSV)](https://www.kaggle.com/datasets/maher3id/restaurant-reviewstsv)\n- Format: `.tsv` file\n- Records: 1000 reviews\n- Columns:\n  - `Review` (text)\n  - `Liked` (binary label)\n\n---\n# 📧 Spam Detection – Deep Learning (NLP Case Study)\n\nThis project detects whether a given SMS message is **Spam** or **Not Spam (Ham)** using Deep Learning and NLP.  \nIt applies preprocessing, tokenization, and an LSTM-based model to classify messages.\n\n---\n\n## 🧠 Project Workflow\n\n### 1. 📥 Importing Data \u0026 Libraries\n- Pandas, NumPy\n- NLTK for text preprocessing\n- TensorFlow / Keras for DL model\n- Matplotlib / Seaborn for visualization\n\n---\n\n### 2. 🧹 Data Cleaning \u0026 Preprocessing\nSteps applied on the text:\n- Lowercasing\n- Removing punctuation \u0026 numbers\n- Removing stopwords\n- Tokenization\n- Padding sequences\n\n---\n\n### 3. 🗃️ Dataset Info\n- **Source**: [Kaggle – SMS Spam Collection Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset)\n- **Size**: 5572 messages\n- **Classes**:\n  - `ham` → Not Spam\n  - `spam` → Spam\n\n---\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgehad-ahmed30%2Fnatural-language-processing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgehad-ahmed30%2Fnatural-language-processing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgehad-ahmed30%2Fnatural-language-processing/lists"}