@inproceedings{jafari-etal-2025-psychological,
title = "Psychological Health Chatbot, Detecting and Assisting Patients in their Path to Recovery",
author = "Jafari, Sadegh and
Zare, Mohammad Erfan and
Vishte, Amireza and
Melike, Mirzae and
Amiri, Zahra and
Mohammadparast, Sima and
Eetemadi, Sauleh",
editor = "El-Haj, Mo",
booktitle = "Proceedings of the 1st Workshop on NLP for Languages Using Arabic Script",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.abjadnlp-1.8/",
pages = "64--77",
abstract = "Mental health disorders such as stress, anxiety, and depression are increasingly prevalent globally, yet access to care remains limited due to barriers like geographic isolation, financial constraints, and stigma. Conversational agents or chatbots have emerged as viable digital tools for personalized mental health support. This paper presents the development of a psychological health chatbot designed specifically for Persian-speaking individuals, offering a culturally sensitive tool for emotion detection and disorder identification. The chatbot integrates several advanced natural language processing (NLP) modules, leveraging the ArmanEmo dataset to identify emotions, assess psychological states, and ensure safe, appropriate responses. Our evaluation of various models, including ParsBERT and XLM-RoBERTa, demonstrates effective emotion detection with accuracy up to 75.39{\%}. Additionally, the system incorporates a Large Language Model (LLM) to generate messages. This chatbot serves as a promising solution for addressing the accessibility gap in mental health care and provides a scalable, language-inclusive platform for psychological support."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jafari-etal-2025-psychological">
<titleInfo>
<title>Psychological Health Chatbot, Detecting and Assisting Patients in their Path to Recovery</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sadegh</namePart>
<namePart type="family">Jafari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohammad</namePart>
<namePart type="given">Erfan</namePart>
<namePart type="family">Zare</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Amireza</namePart>
<namePart type="family">Vishte</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mirzae</namePart>
<namePart type="family">Melike</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zahra</namePart>
<namePart type="family">Amiri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sima</namePart>
<namePart type="family">Mohammadparast</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sauleh</namePart>
<namePart type="family">Eetemadi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-01</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 1st Workshop on NLP for Languages Using Arabic Script</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mo</namePart>
<namePart type="family">El-Haj</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Abu Dhabi, UAE</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Mental health disorders such as stress, anxiety, and depression are increasingly prevalent globally, yet access to care remains limited due to barriers like geographic isolation, financial constraints, and stigma. Conversational agents or chatbots have emerged as viable digital tools for personalized mental health support. This paper presents the development of a psychological health chatbot designed specifically for Persian-speaking individuals, offering a culturally sensitive tool for emotion detection and disorder identification. The chatbot integrates several advanced natural language processing (NLP) modules, leveraging the ArmanEmo dataset to identify emotions, assess psychological states, and ensure safe, appropriate responses. Our evaluation of various models, including ParsBERT and XLM-RoBERTa, demonstrates effective emotion detection with accuracy up to 75.39%. Additionally, the system incorporates a Large Language Model (LLM) to generate messages. This chatbot serves as a promising solution for addressing the accessibility gap in mental health care and provides a scalable, language-inclusive platform for psychological support.</abstract>
<identifier type="citekey">jafari-etal-2025-psychological</identifier>
<location>
<url>https://aclanthology.org/2025.abjadnlp-1.8/</url>
</location>
<part>
<date>2025-01</date>
<extent unit="page">
<start>64</start>
<end>77</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Psychological Health Chatbot, Detecting and Assisting Patients in their Path to Recovery
%A Jafari, Sadegh
%A Zare, Mohammad Erfan
%A Vishte, Amireza
%A Melike, Mirzae
%A Amiri, Zahra
%A Mohammadparast, Sima
%A Eetemadi, Sauleh
%Y El-Haj, Mo
%S Proceedings of the 1st Workshop on NLP for Languages Using Arabic Script
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F jafari-etal-2025-psychological
%X Mental health disorders such as stress, anxiety, and depression are increasingly prevalent globally, yet access to care remains limited due to barriers like geographic isolation, financial constraints, and stigma. Conversational agents or chatbots have emerged as viable digital tools for personalized mental health support. This paper presents the development of a psychological health chatbot designed specifically for Persian-speaking individuals, offering a culturally sensitive tool for emotion detection and disorder identification. The chatbot integrates several advanced natural language processing (NLP) modules, leveraging the ArmanEmo dataset to identify emotions, assess psychological states, and ensure safe, appropriate responses. Our evaluation of various models, including ParsBERT and XLM-RoBERTa, demonstrates effective emotion detection with accuracy up to 75.39%. Additionally, the system incorporates a Large Language Model (LLM) to generate messages. This chatbot serves as a promising solution for addressing the accessibility gap in mental health care and provides a scalable, language-inclusive platform for psychological support.
%U https://aclanthology.org/2025.abjadnlp-1.8/
%P 64-77
Markdown (Informal)
[Psychological Health Chatbot, Detecting and Assisting Patients in their Path to Recovery](https://aclanthology.org/2025.abjadnlp-1.8/) (Jafari et al., AbjadNLP 2025)
ACL
- Sadegh Jafari, Mohammad Erfan Zare, Amireza Vishte, Mirzae Melike, Zahra Amiri, Sima Mohammadparast, and Sauleh Eetemadi. 2025. Psychological Health Chatbot, Detecting and Assisting Patients in their Path to Recovery. In Proceedings of the 1st Workshop on NLP for Languages Using Arabic Script, pages 64–77, Abu Dhabi, UAE. Association for Computational Linguistics.