Case StudyText Emotion Recognition
Teaching machines to detect emotions hidden in text.
An NLP model that goes beyond positive/negative classification — detecting specific emotions like joy, sadness, anger, and fear with probability scores.
Collaborators
Overview
Fine-grained emotion detection beyond binary sentiment.
Built with Logistic Regression and TF-IDF vectorization. The model outputs probability distributions across multiple emotion classes, deployed as a Flask web application for real-time analysis.
Outcome
- Multi-class emotion detection with probability scoring
- Logistic Regression model with TF-IDF vectorization
- Flask web app for real-time text analysis