Authors :
Aditya Kataria; Riva Desai; Hassan Kapadia; Rohan Patel; Aashka Maru; Bhumika Shah; Dhatri Pandya
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/mvd9ssvn
Scribd :
https://tinyurl.com/4y2y7suy
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR1607
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.
Keywords :
Sentiment Analysis, Mental Health, Machine Learning, Support Vector Machines, Conversational AI, Natural Language Processing.
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.
Keywords :
Sentiment Analysis, Mental Health, Machine Learning, Support Vector Machines, Conversational AI, Natural Language Processing.
