SENTI Aid: Sentiment Analysis on Personal Relations and Aiding Mental Health

Abstract

This research examined the ability of a novel mobile application designed to provide proactive mental health support by analyzing the user’s conversations and recommends interventions accordingly. Employing sentiment analysis of the user's recorded discussions with designated social contacts (parents, siblings, partner), the application identifies indicators of potential issues in mental health. A personalized chatbot then interacts with the user, offering feedback based on the sentiment analysis and engages in positive conversation to uplift user’s mood. Additionally, the system monitors the user's application activities and chatbot interaction patterns, detecting atypical behaviors for further feedback or prompting emergency alerts to pre-defined contacts. The research employed a two-phased approach: an initial pilot study with simulated data to refine the sentiment analysis and chatbot algorithms, followed by a validation study with a limited user group, utilizing actual conversation recordings. Analysis of the pilot data showed promising accuracy in identifying negative sentiments, while the validation study demonstrated a significant improvement in positive engagement and self- reported well-being among participants. Overall, the findings suggest that this multi-faceted approach using sentiment analysis and conversational AI holds potential for early detection and proactive intervention in mental health issues, justifying further investigation and refinement for broader implementation.