Emotion-Detection-Learning-Support-Engine

AI-powered Emotion Detection and Learning Support System using BiLSTM, TensorFlow, and NLP.# Emotion Detection & Learning Support Engine

Project Description

The Emotion Detection & Learning Support Engine is an AI-powered application that analyzes user text and detects emotions using a BiLSTM deep learning model. Based on the detected emotion, the system provides personalized learning guidance to support learners effectively.

Features

• Emotion Detection using BiLSTM
• Text Preprocessing using NLP techniques
• Personalized Learning Support
• Analytics Dashboard using Plotly
• CSV Logging of Predictions
• Emotion Visualization

Technologies Used

• Python
• TensorFlow
• Keras
• Pandas
• NumPy
• Scikit-learn
• Plotly
• Streamlit
• Google Colab

Supported Emotions

• Anger
• Fear
• Joy
• Love
• Sadness
• Surprise

Model Performance

• Training Accuracy: 96.87%
• Validation Accuracy: 87.06%

Project Workflow

1. Load and preprocess the dataset.
2. Convert text into sequences using Tokenizer.
3. Train a BiLSTM model for emotion classification.
4. Predict emotions from user input.
5. Provide AI-based learning guidance.
6. Visualize emotion distribution using graphs.
7. Store prediction logs in CSV format.

Future Enhancements

• Integration with Gemini AI for advanced guidance.
• Deployment using Streamlit Cloud.
• Addition of BERT model for comparison.
• Real-time emotion analytics dashboard.

Author

** MEDIDHI V NAGA SRI SURYA SATYA SANTHOSHI **