Depression screening web application with an integrated chatbot feature.

Abstract

The accuracy and effectiveness of mental health screening systems depend largely on the quality of the data and the models used to interpret user inputs. Traditional screening methods, such as fixed questionnaires, often lack emotional depth and fail to provide personalized assessments. This creates a significant challenge, especially when detecting depression through open-ended responses, as understanding emotional context and sentiment in text requires robust Natural Language Processing (NLP) and machine learning techniques. This study presents MindEase, a depression screening web application that combines structured questionnaires with free-text responses analyzed through sentiment classification and a conversational chatbot. To achieve accurate mental health assessments, the system uses machine learning models trained on publicly available datasets, enhanced by TF-IDF feature extraction and sentiment scoring through a logistic regression classifier. Furthermore, the system integrates the Gemini API to dynamically generate personalized follow-up questions, improving the engagement and depth of user interactions. Experimental results show that the implemented model achieves a high level of accuracy in classifying sentiment, aiding in detecting potential signs of depression. By combining structured and unstructured data analysis with real-time feedback, MindEase offers a more adaptive, user friendly, and emotionally aware mental health support tool. The system also adheres to privacy standards and aims to lower the barrier for individuals seeking early mental health assistance.