LSTM-based NLP model that classifies human emotions from text — built with PyTorch
The Problem
Understanding human emotions from text is an important task in Natural Language Processing. This project builds a Deep Learning model that automatically classifies emotions from textual data — useful in chatbots, customer feedback analysis, and mental health monitoring.
How It Works
Input Text
Raw text input from the dataset containing emotional expressions.
Tokenization & Padding
Text normalized, tokenized, and padded to a fixed length of 50 tokens.
Embedding Layer
Words converted to dense vector representations in continuous space.
LSTM Layer
Long Short-Term Memory network captures sequential patterns and context.
Fully Connected Layer
Learns complex feature combinations from LSTM output representations.
Emotion Prediction
Outputs one of 6 emotion classes via softmax classification.
Model Performance
See It In Action
Built With
Preprocessing & Training
Data Preprocessing
Training Configuration
Project Roadmap
Add attention mechanism to improve performance and interpretability
Use Bidirectional LSTM for capturing both forward and backward context
Integrate Transformer-based models like BERT for state-of-the-art results
Deploy the model as a real-time web application with an interactive interface
About Me
Tirth Patel
I am a passionate and disciplined learner with a strong interest in Deep Learning, NLP, and AI. I actively work on real-world ML projects to strengthen my practical understanding of neural networks and modern AI systems. I believe in learning concepts deeply by implementing them from scratch.