Abstract

With its potential for early diagnosis, research on mental health monitoring is an active area and automatic analysis is an important component of such a system. However most research involves simply detecting presence/absence of depression, which is not sufficiently granular for practical application. We propose the development of a interactive chatbot which would classify user responses into four severity levels of depression-Minimal, Mild, Moderate and Severe. We developed an NLP pipeline using lemmatization and TF-IDF vectorization to train and compare a Logistic Regression model with a fine-tuned Support Vector Machine. Results indicate that the SVM model achieved 74.36% accuracy among other algorithms and could be used as a suitable engine to provide an interactive conversational interface to asses user's current stress level in real time.

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