Social media profile analysis for predicting the psychological state

Abstract

In recent years, social media platforms have become a rich source of user-generated content, providing valuable insights into individuals' thoughts, emotions, and mental well-being. SOCIAL MEDIA PROFILE ANALYSIS FOR PREDICTING THE PSYCHOLOGICAL STATE presents a comprehensive review of social media profile analysis techniques used for detecting mental states. The objective is to explore the potential of analyzing social media profiles to identify indicators of various mental health conditions, including depression, anxiety, and stress. The review encompasses a wide range of methodologies employed in the field, including natural language processing, sentiment analysis, machine learning, and data mining. By leveraging these techniques, researchers have developed approaches to automatically extract valuable information from social media profiles, such as textual posts, images, and social connections, to infer the mental states of individuals. SOCIAL MEDIA PROFILE ANALYSIS FOR PREDICTING THE PSYCHOLOGICAL STATE highlights the challenges and limitations associated with social media profile analysis for mental state detection, including issues of data privacy, ethical considerations, and the need for context-aware interpretation. Additionally, it discusses the potential benefits and applications of these techniques in various domains, such as early intervention, mental health monitoring, and public health policy. The main objective of SOCIAL MEDIA PROFILE ANALYSIS FOR PREDICTING THE PSYCHOLOGICAL STATE is to predict the psychology of human mind on suicide using various methods. Methods like Bag-of-Words, Google BERT etc. used to find out the emotional intelligence on the social media data.