A machine learning NLP system designed to analyze text input and classify emotional tone and potential risk patterns using semantic sentence embeddings and supervised classification techniques.
Built as an applied learning project focused on behavioral language analysis, emotional context recognition, and real-world NLP experimentation.
Core Features
- Built with Python and scikit-learn
- Uses Sentence Transformers (
all-MiniLM-L6-v2) for semantic text embeddings - Trained on a labeled emotional dataset
- Multi-class emotion and risk classification system
- Confidence score prediction outputs
- Streamlit interface for real-time interaction and testing
- Dataset preprocessing and embedding experimentation
- Model testing and evaluation workflows
Technical Focus Areas
This project explored:
- natural language processing fundamentals,
- sentence embeddings,
- supervised machine learning,
- emotional language classification,
- semantic similarity,
- text preprocessing,
- and behavioral pattern analysis through language.
Other Work
- Dataset preparation and manual labeling for NLP classification tasks
- Experimentation with text preprocessing pipelines
- Embedding model integration and testing
- Model evaluation and prediction analysis
- Interactive Streamlit testing interface development
In Progress / Future Work
Future development goals include:
- expanding dataset quality and scale,
- improving classification accuracy,
- integrating deeper NLP systems,
- experimenting with transformer-based models,
- and developing more advanced human-centered AI systems focused on language, emotional context, and behavioral understanding.
GitHub: Project Link: Emotion & Risk Classifier (NLP System)
The Clear Mind Project 
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