Paper Key : IRJ************714
Author: Abhinav Kumar Singh,Aditya Kumar Jha,Aditya Kumar,R Adhiseshan, Dr. B Selvapriya
Date Published: 12 Apr 2024
Abstract
Previous studies have highlighted that individuals with mental health disorders exhibit identifiable patterns on social media. They may participate in screening surveys, engage in community discussions on platforms like Twitter, or be active members of online forums. These patterns make them distinguishable from regular users based on their language use and online behavior. Several automated detection methods have been developed to aid in the identification of individuals experiencing depression through their social media activity. Furthermore, some authors have suggested a correlation between activities on Social Networking Sites and low self-esteem, particularly among young people and adolescents. In our project, stress analysis is conducted using algorithms, specifically GoogleNet as the established system and Inception V3 as the proposed system. The results demonstrate that the proposed GoogleNet algorithm outperforms the existing Inception V3 in terms of accuracy.
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