General-Purpose Sentiment Analysis is a deep learning project aimed at analyzing and classifying sentiment in text data across various domains. The project leverages a hybrid architecture combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to provide accurate sentiment predictions for a wide range of applications.
The project adopts a hybrid approach, utilizing CNNs for feature extraction and LSTMs for capturing sequential dependencies in the text data. By training the model on a diverse dataset comprising GoEmotions and DailyDialog, it achieves robust sentiment analysis capabilities across different emotional nuances.
- Multi-class sentiment classification across 6 emotions including happy, sad, fear, anger, surprise and neutral.
- Comprehensive text preprocessing pipeline ensuring clean and standardized input data.
- Strategic handling of class imbalance to optimize model generalization.
- Versatile model architecture adaptable to various text data domains and applications.
- Python
- TensorFlow/Keras
- Clone the repository:
git clone https://github.com/yourusername/sentiment-analysis-hybrid.git - Install the required dependencies:
pip install -r requirements.txt - Train the model using the provided dataset or your custom data.
- Prepare your text data by following the provided preprocessing guidelines.
- Train the sentiment analysis model using the prepared dataset.
- Evaluate the model's performance using appropriate metrics.
- Deploy the trained model in your desired application or environment for sentiment analysis tasks.
Contributions are welcome! If you have any suggestions, feature requests, or bug reports, please open an issue or submit a pull request.
- Special thanks to the GoEmotions and DailyDialog datasets for providing valuable training data.
- Inspired by similar projects in the field of sentiment analysis and natural language processing (NLP).
For inquiries or collaborations, please contact me at miteshgupta2711@gmaail.com.