صادق جعفري

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 project 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 is a promising solution for addressing the accessibility gap in mental health care and provides a scalable, language-inclusive platform for psychological support.

عامل گفتاري روانشناس , پردازش زبان طبيعي , يادگيري عميق , مدل‌هاي زباني

Psychologist Chatbot , Natural Language Processing , Deep Learning , Language models

Sadegh Jafari

Sauleh Eetemadi