Overview This project aims to develop a system for detecting emotions in text data using natural language processing (NLP) techniques. The system analyzes text input and predicts the emotion conveyed in the text. Emotion detection has various applications in sentiment analysis, customer feedback analysis, mental health monitoring, and more.
Features Emotion Classification: The system classifies text into different emotion categories such as happy, sad, angry, surprised, etc. Pre-trained Models: Utilizes pre-trained NLP models such as BERT, GPT, or LSTM for emotion detection tasks. Customization: Provides options to fine-tune pre-trained models or train new models on specific datasets for better performance. API Integration: Can be integrated into other applications through APIs for real-time emotion detection. Web Interface: Includes a simple web interface for users to input text and receive emotion predictions.
Training Custom Models If you want to train custom models or fine-tune existing ones:
Prepare your dataset in the required format. Modify the training scripts or create new ones as per your requirements. Train the models using the provided scripts.
Data Set Link - https://www.kaggle.com/jonathanoheix/face-expression-recognition-dataset