This project aims to classify text sentiment (positive, negative, or neutral) using a neural network model built with TensorFlow and Keras. It processes text data and applies deep learning techniques for sentiment analysis.
- Preprocessing of text data (tokenization, padding)
- Neural network training for classification
- Model evaluation using precision, recall, and F1-score
- Graphical representation of training progress (loss & accuracy)
├── data/ # Dataset files ├── models/ # Saved model weights ├── src/ # Source code │ ├── preprocess.py # Data cleaning and tokenization │ ├── train.py # Model training script │ ├── evaluate.py # Performance evaluation ├── requirements.txt # Dependencies └── README.md # Project documentation