• Amaram Ramya
• Dr. M.Naga lakshmi

Psychological stress is a threat to human health. It is not an easy habit to detect stress in a timely and careful manner. Despite the popularity of social media, people are accustomed to sharing their daily activities and interactions with friends on social media platforms to receive social media information online when they detect stress. In this post, we use a large database of real platforms to accurately read the social interaction between consumer stresses, knowing that user stress is closely linked to the social networks of friends. First, we identify many texts related to stress, perceptions, and social characteristics from different sources and propose a new hybrid model: an example of an invention scheme developed by Evolution Neural Network to use the content on Twitter and information about stress-induced interactions. The results of the experiment showed that the proposed model can increase the detection efficiency in the F1 brand by 6-9%. After a more in-depth analysis of the data on social interactions, we found an interesting phenomenon, that the number of social networks is 14% higher than the number of users who are less connected (ie, without delta connections). Compressed social networks of friends have fewer and more complex relationships than unauthorized users.

Detecting Social Network Users Stress based on Attributes Categorization

"Detecting Social Network Users Stress based on Attributes Categorization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 2, page no.2170-2174, February-2021, Available :http://www.jetir.org/papers/JETIR2102265.pdf

"Detecting Social Network Users Stress based on Attributes Categorization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 2, page no. pp2170-2174, February-2021, Available at : http://www.jetir.org/papers/JETIR2102265.pdf