Saathi — AI Mental Health Companion

Problem

The thesis started from a frustration I couldn’t shake: getting actual mental health support is slow, expensive, and hard to access. For my final year, I wanted to see how close you could get to something useful using NLP — specifically around detecting bipolar disorder signals from text.

The question was whether a conversational system could identify those signals accurately enough to be worth building.

Approach

Sentiment & mood detection

Trained a CNN + LSTM hybrid on over 100,000 Reddit posts from mental health communities (r/bipolar, r/depression, r/mentalhealth). The CNN layer extracts local textual features (word patterns, n-gram signals); the LSTM layer captures long-range sequential dependencies in how mood states evolve across a conversation.

Multi-class classification across: euthymia, hypomanic, manic, depressive, and mixed states.

Conversational interface

RASA handles dialogue management — intent classification, entity extraction, and conversation state. The sentiment model runs as a custom RASA component, feeding mood state predictions into the dialogue policy.

Response strategies vary based on detected state: de-escalation for manic states, validation and gentle activation for depressive states, psychoeducation during euthymia.

Dataset

• r/bipolar, r/depression, r/mentalhealth posts (2018–2022)
• Labels derived from post flair and manual annotation for a validation subset
• Preprocessing: tokenization, lemmatization, stopword removal, slang normalization

Tech Stack

• ML: TensorFlow (CNN + LSTM), custom preprocessing pipeline
• NLP: RASA (dialogue management), NLTK, spaCy
• Backend: Python, Flask API
• Deployment: Local + university server

Results

• 95.51% accuracy on bipolar disorder detection (held-out test set)
• 4th place at IIT Bombay RESCON — national-level research competition
• Published IEEE 2023 — peer-reviewed conference paper
• System demonstrated live to faculty evaluation panel

What I learned

This was my first real NLP project at any scale. The biggest challenge was label quality: Reddit post flair is noisy, and a model trained on noisy labels learns the noise. We added a secondary annotation pass using a small crowd-sourced validation set — carefully labelled — which helped calibration a lot.

RASA’s dialogue management is powerful but opinionated. Integrating a custom PyTorch/TF component required wrapping it as a RASA custom component — good architectural lesson in interface design.

The accuracy numbers are promising, but I’m careful about overstating what this means clinically. A detection model is a screening signal, not a diagnosis — and the thesis was careful to frame it that way.