Building a Digital Psychologist: An AI-Based Approach to Mental Health Support Using NLP And Therapeutic Models

Abstract

As mental health problems become more common, there is a need for easily accessible, effective, and customized support systems. This study presents the development and deployment of an AI-powered chatbot that raises awareness of mental health issues and can act as a digital psychologist. To evaluate users’ mental health and offer personalized treatment recommendations, the chatbot engages with them using sophisticated natural language processing and machine learning techniques. The architecture of the chatbot is carefully designed to integrate multiple essential components: a user-friendly interface, robust data privacy measures, and a comprehensive database of therapeutic content curated by mental health professionals. The chatbot's ability to process natural language allows it to precisely understand and evaluate user inputs, providing tailored recommendations and feedback. This chatbot leverages natural language processing (NLP) techniques to understand and respond to user queries, employing sentiment analysis and intent recognition to tailor its responses. Machine learning models and algorithms, such as supervised and unsupervised learning, are utilized to classify user inputs and generate appropriate responses. Additionally, the chatbot incorporates a recommendation system to suggest coping strategies and resources, enhancing its utility as a mental health tool. Ethical considerations, such as data privacy, bias mitigation, and crisis management protocols, are also addressed to ensure the chatbot’s reliability and safety. Preliminary evaluations indicate that the chatbot effectively engages users and provides relevant mental health support, demonstrating its potential as a valuable resource in mental health care

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 1 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.