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What it does
Mental health insights are often hidden in plain sight — in the words people use online or in journals. But making sense of that text at scale is a challenge. Manual methods don’t scale, and most models fall short in precision.
MindSpectrum is a machine learning-based text classification model built to detect mental health states through linguistic patterns. Trained on labeled data, it evaluates user-generated text to classify psychological conditions with impressive precision and consistency. The model has been tested across multiple classifiers — including Logistic Regression, SVM, Naive Bayes, Decision Trees, and ensemble Voting Classifiers — with top-performing models like SVM achieving over 92% accuracy. It outputs clear metrics like precision, recall, and F1-score, making it suitable for scalable deployment in mental health research or monitoring tools.
Whether used as a backend model for a larger mental health platform or as a research tool for analyzing text sentiment and emotional state, MindSpectrum delivers reliable classification — fast and efficiently.