Open AccessMental Health Assist and Diagnosis Conversational Interface using Logistic Regression Model for Emotion and Sentiment Analysis
Author(s) -
S Moulya,
T R Pragathi
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2161/1/012039
Subject(s) - chatbot , computer science , duration (music) , human–computer interaction , dialog system , logistic regression , mental health , interface (matter) , domain (mathematical analysis) , user interface , baseline (sea) , applied psychology , world wide web , multimedia , psychology , artificial intelligence , machine learning , psychotherapist , art , mathematical analysis , oceanography , literature , mathematics , bubble , maximum bubble pressure method , parallel computing , dialog box , geology , operating system
The aim of this work was to create a fully functional AI-ML based conversational agent that behaves like a real time therapist which analyses the user’s emotion at every step and provides appropriate responses and feedback. AI chatbots, although fairly new to the domain of mental health, can help in destigmatizing seeking help, and are more easily accessible to everyone, at any time. Chatbots provide an effective way to communicate with a user and offer helpful emotional support in a more economical way. While making regular psychiatric visits often require a fixed duration/appointment which can be time consuming and is restricted to a fraction of the day, the proposed chatbot can keep track of your health on the go at any time. The application will have a self-healing kit suggesting various exercises, both mental and physical that the user may implement in his day-to-day life. The study below goes into further detail on the major insinuations for future chatbot agent design and assessment