• Qauseen Mulla
• Aslam J Karjagi
• Sayed Abulhassan Quadri

Stress is a pervasive mental health issue affecting individuals worldwide and its timely detection is important for effective intervention and support. Stress detection in this project harnesses the vast amount of data usually termed as big data generated by social media and identifies the signs of stress in individuals. By analyzing the data available on social media, valuable insights can be gleaned about mental wellbeing of the individual. Machine Learning algorithms like CNN (Convolutional Neural Network) and FGM (Factor Graph Model) are employed to detect linguistic patterns indicating stress in an individual. The stress levels can be monitored in the forms of graphs for individuals as well as whole community.

Stress, Mental health, SQL, MVC, CNN, FGM.

"Stress detection using social media interactions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.d306-d310, September-2023, Available :http://www.jetir.org/papers/JETIR2309328.pdf

"Stress detection using social media interactions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppd306-d310, September-2023, Available at : http://www.jetir.org/papers/JETIR2309328.pdf