Depression Intensity Estimation via Social Media Data Using a Deep Learning Framework

Depression Intensity Estimation via Social Media Data Using a Deep Learning Framework

Mr. Gaurav Kawade

Mr. Yash Thakare

Mr. Divyaratna Bhusari

Students, Department of Computer Engineering

Prof. Shiv Shankar

Assistant Prof.  Computer Department ,

Padmabhooshan Vasantdada Patil Institute of Technology, Pune

Abstract-Stress and depression are the most widely perceived and incapacitating mental problems that affect society rightly. Automated health monitoring systems may be key and important for improving the framework of sadness and stress recognition using social networking. Sentiment analysis refers to the use of natural language processing and content mining approaches to plan sentiment or opinion recognition. Sensible computing is the examination and advancement of frameworks and gadgets that can understand, understand, process, and replicate human effects. Sentiment analysis and deep learning techniques can provide powerful algorithms and frameworks for targeted assessment and observation of psychological issues, and depression and stress in particular. The use of sentiment analysis and deep learning methods for depression and stress detection and monitoring is discussed. In addition, the basic scheme of an inclusive multimodal framework for investigating stress and depression, including appraisal investigation and emotion processing strategies, is studied. In particular, the paper explores the fundamental issues and approaches such frameworks comparatively.

Index Terms - Deep Learning, Health, Stress and Depression, Sentiment Analysis, Social Media.