ESAI: An AI-Based Emotional Support System to Assist Mental Health Disorders

Gabriel Serrano, Daehan Kwak

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Every year more and more people are struggling to handle a wide variety of mental health disorders, such as anxiety and depression. However, as the number of people in need of assistance increases, the number of resources available to them has continued to decrease. The goal of this research is to develop an Emotional Support AI (ESAI) system, an additional resource for those unable to obtain the help and information they need. The ESAI has been trained to classify text based on the Naive Bayes Classification model. The model was trained using 160,000 Reddit posts, which were collected using web scrapping, where users have discussed their experiences with mental health. ESAI provides users with a friendly user-interface from which they can discuss their mental health concerns. The user can choose whether to communicate through typing or through real-time speech recognition. ESAI works by hosting 'sessions', in which it will log communications between itself and the user to check for any potential flags that may indicate the user is experiencing symptoms of one or many mental health disorder(s). These sessions can be used by the user for venting or to seek information regarding a variety of mental health disorders. If the probability that the user is experiencing a mental health disorder is higher than a specific threshold, the user is provided with general resources and contacts regarding the specified disorder. The user will also be provided with a mental health evaluation report at the end of each session upon request. Currently, results show that ESAI can classify mental health disorders with seventy-percent accuracy.

Original languageEnglish
Title of host publicationProceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1348-1354
Number of pages7
ISBN (Electronic)9798350327595
DOIs
StatePublished - 2023
Event2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 - Las Vegas, United States
Duration: 24 Jul 202327 Jul 2023

Publication series

NameProceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023

Conference

Conference2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
Country/TerritoryUnited States
CityLas Vegas
Period24/07/2327/07/23

Keywords

  • Artificial Intelligence (AI)
  • Emotional Support
  • Machine Learning (ML)
  • Mental Health
  • Natural Language Processing (NLP)
  • Text Classification

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