TY - GEN
T1 - ESAI
T2 - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
AU - Serrano, Gabriel
AU - Kwak, Daehan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Artificial Intelligence (AI)
KW - Emotional Support
KW - Machine Learning (ML)
KW - Mental Health
KW - Natural Language Processing (NLP)
KW - Text Classification
UR - http://www.scopus.com/inward/record.url?scp=85191146279&partnerID=8YFLogxK
U2 - 10.1109/CSCE60160.2023.00226
DO - 10.1109/CSCE60160.2023.00226
M3 - Conference contribution
AN - SCOPUS:85191146279
T3 - Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
SP - 1348
EP - 1354
BT - Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 July 2023 through 27 July 2023
ER -