Project Title

NeuroNLP — Cognitive & Emotional Text Analysis Engine

Project Link

- Overview: NeuroNLP is a full-stack, microservice-based text analysis platform engineered to evaluate human written communication across cognitive, emotional, and structural dimensions. The system processes user text in real-time through an asynchronous Python backend to compute a composite "Brain Cognition Score" alongside multi-faceted analytics visualized in an interactive Next.js chat interface.

- Coherence & Logical Flow Engine: Converts sentences into dense vector embeddings using all-MiniLM-L6-v2 (Sentence-Transformers) and evaluates sentence-to-sentence topical continuity via pairwise cosine similarity matrices.

- Granular Emotion Classifier: Utilizes a fine-tuned roberta-base-go_emotions transformer model running on PyTorch to detect probability distributions across 27 distinct emotional states.

- Lexical Diversity Pipeline: Computes a normalized Type-Token Ratio (TTR) using spaCy tokenization pipelines to evaluate vocabulary richness and prevent stylistic redundancy.

- Automated Grammar Scoring: Integrates language-tool-python to identify syntactic errors and derive an inverted error-rate metric normalized to a 100-point scale.

- Backend Microservice: Developed with Python and FastAPI (running Uvicorn) to execute asynchronous ML inference and return structured JSON analytics payloads.

- Frontend Application: Built with Next.js 14 (App Router), React 18, and TypeScript, styled via Tailwind CSS, and rendering real-time analytical charts using Recharts.

- ML Infrastructure: Powered by PyTorch, Hugging Face Transformers, Sentence-Transformers, and spaCy.