https://github.com/ajitashwath/sarcasm-detection

https://github.com/ajitashwath/sarcasm-detection

bert-model nlp-machine-learning tensorflow

Last synced: 3 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/ajitashwath/sarcasm-detection
• Owner: ajitashwath
• License: mit
• Created: 2024-11-16T17:30:28.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-06T08:18:38.000Z (over 1 year ago)
• Last Synced: 2026-04-15T09:44:23.648Z (3 months ago)
• Topics: bert-model, nlp-machine-learning, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 1.8 MB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Sarcasm Detection using Deep Learning  

This project combines traditional machine learning and advanced deep learning techniques to accurately detect sarcasm in text. By leveraging both TF-IDF-based Random Forest models and the power of BERT, it ensures robust performance across various datasets.

## Features  

### 1. Advanced Text Preprocessing  

- Custom `TextPreprocessor` Class: Handles text cleaning, lemmatization, and tokenization.  

- Stopword Removal: Removes common words that don’t contribute to meaning.  

- Special Character Handling: Filters out unnecessary symbols and punctuations.  

### 2. Dual Model Architecture  

- Traditional ML Model: Uses TF-IDF features and a Random Forest classifier for sarcasm detection.  

- Deep Learning Model: Implements a fine-tuned BERT model for contextual understanding.  

- Ensemble Prediction: Combines predictions from both models for improved accuracy.  

### 3. BERT Integration  

- Pre-Trained Model: Uses BERT for advanced feature extraction.  

- Custom Dataset Class: Prepares input for BERT with attention masks and tokenization.  

- Context Understanding: Captures nuanced meanings for accurate sarcasm detection.  

### 4. Better Feature Engineering  

- TF-IDF Representation: Replaces basic CountVectorizer for better feature weighting.  

- N-Gram Support: Includes uni-grams, bi-grams, and tri-grams for richer features.  

- BERT Word Embeddings: Utilizes BERT's contextual embeddings for deep learning.  

### 5. Improved Training Process  

- Train/Validation Split: Ensures reliable performance evaluation.  

- Cross-Validation: Supports robust model tuning.  

- Learning Rate Optimization: Implements dynamic learning rates for faster convergence.  

- Dropout Regularization: Prevents overfitting in deep learning models.  

### 6. Additional Functionality  

- Device Support: Compatible with both CPU and GPU for training and inference.  

- Comprehensive Evaluation Metrics: Includes accuracy, precision, recall, F1-score, and more.  

- Flexible Predictions: Choose between traditional ML, BERT, or ensemble methods.  

## Requirements  

- Python 3.12+

- Jupyter Notebook

- Libraries:  

  - TensorFlow / PyTorch  

  - NLTK  

  - Transformers  

  - Scikit-Learn

  - re

  - Matplotlib

  - Collections

  - Numpy

  - Pandas

## Installation 

1. Clone the repository

   ```bash

   git clone https://github.com/ajitashwathr10/Sarcasm-Detection.git

   cd Sarcasm-Detection

   ```

2. Set up a virtual environment (Optional)

   ```bash

   python -m venv venv

   source venv/bin/activate       # On Linux/Mac

   venv\Scripts\activate          # On Windows

   ```

3. Install Dependencies

   ```bash

   pip install -r requirements.txt

   ```

4. Download Pre-Trained BERT Model

   - This project uses the Hugging Face Transformers library. Ensure you have internet access when running the model to automatically download the pre-trained BERT weights.

5. Verify Installation

   ```bash

   python test_installation.py

   ```

### Additional Notes

- For GPU support, ensure you have CUDA installed and install the GPU-compatible version of TensorFlow or PyTorch

  ```bash

  pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117

  ```

  ```bash

  pip install tensorflow-gpu

  ```

Now you're ready to start detecting sarcasm!

## Usage

1. Preprocess Data

   - Prepare your dataset using the `TextPreprocessor` class to clean and tokenize text.

2. Train Models

   - Train the traditional and deep learning models using the provided training pipelines.

3. Make Predictions

   - Use the ensemble or individual models to predict sarcasm in new textual data.

- Example:

  ```bash

  from sarcasm_detector import SarcasmDetector

  detector = SarcasmDetector()

  detector.train("path/to/dataset.csv")

  predictions = detector.predict(["This is such a great day!", "Oh, what a surprise..."])

  print(predictions)

  ```

## Contributing

We welcome contributions to enhance this project. Feel free to submit pull requests or raise issues!

## License

This project is licensed under the MIT License. See the LICENSE file for details.