Early Prediction of Depression by using Text and Deep Learning

Abstract

Introduction: In today’s modern era, with the advancement of the world in various social media platforms, it has been greatly observed that the mental health problems of people have increased drastically. A person who looks happy can be suffering from severe mental health problems. Many people suffer from various mental health problems due to workload and social media influence as well as daily challenges, which often lead them towards significant harmful decisions. The increase in mental health diseases, like depression, bipolar disorder, and anxiety, makes the need for an in-depth analysis of an examination in this field.

Objectives: This paper surveys the comparative analysis of the current state of mental health research and the variety of inventions brought to date. It examines the usage of text analysis techniques in the text appearing on social media sites to get a better understanding of the emotions along with the challenges that confront the individual.

Methods: In this, various techniques using which the researchers can trace the modes of expression of mental health issues eventually detect early stages of distress and monitor the impact. This paper reviews the current applications of text analysis in mental health research, challenges in preprocessing unstructured data, and the importance of balancing machine-based analysis with human understanding.

Results: It briefly provides a survey of the importance of analysis using text in the mental health field and how it can benefit improved understanding and care in the said discipline. The findings highlight the importance of integrating these technologies into the current mental health practices to enhance emotional support and self-regulation.

Conclusions: The novelty of the research lies in its in-depth analysis of smart tools specifically designed for monitoring mental health using text, providing insights into the real-world challenges and benefits of using such technologies in everyday settings, beyond what has been explored in earlier studies.