Machine Learning Based Prediction and Recommendation System for Anxiety and Depression: A Comprehensive Survey and Analysis

Abstract

People often express their emotional and mental state through social media platform. This review synthesizes the key findings from recent studies, reporting diverse approaches to the use of machine learning, natural language processing, and multimodal analysis of physiological and behavioral data. By leveraging diverse data types, including text, images, and videos, provide valuable insights into users' mental well-being. Predicating anxiety and depression based on social media content allows for early identification of mental health issues before they become severe. Earlier research was based on findings that social media can serve as a rich source of information for understanding users' emotional conditions. Additionally, discusses the need for automated systems that can detect depression across different age groups, utilizing sentiment analysis and facial expressions. The review paper provides a deep view of the current techniques and provide it's limitative along with the research challenges in the field of mental health issues diagnosis by using ML algorithms.