manupawar6388/Emotion_text-Regression

Emotion Analysis with LSTM

This project implements an LSTM-based neural network for emotion analysis using the emotion_sentimen_dataset.csv.

Features

• Efficient Training: Uses a subset (20,000 samples) of the large dataset for faster experimentation while maintaining good accuracy.
• LSTM Architecture: Embedding -> SpatialDropout -> LSTM -> Dense.
• Deployment Ready: Includes scripts for training and real-time prediction.

Requirements

• Python 3.x
• TensorFlow / Keras
• Pandas, NumPy, Scikit-learn

Files

• train_emotion_lstm.py: Script to preprocess data, build, train, and save the model.
• predict_emotion.py: Script to load the trained model and predict emotions from user input.
• emotion_model.h5: The saved trained model.
• tokenizer.pickle: Saved tokenizer for text processing.
• label_encoder_classes.npy: Saved label encoder classes.

Usage

1. Training

To train the model (if you want to re-train):

python train_emotion_lstm.py

This produces emotion_model.h5 and necessary artifacts.

2. Prediction

To use the model for prediction:

python predict_emotion.py

Type any sentence when prompted, and the model will classify its emotion.

Performance

The model achieves high accuracy (>90%) on the test set even with a reduced dataset size, demonstrating efficiency.