A Machine Learning Approach for Detecting Stress Based On Social Interactions in Social Networks

Stress is a kind of the demand in your body's way of responding to any. It can be based on Both Good and bad experiences. Psychological stress is threatening people’s health. With the reputation of a social media network, people are used to sharing their schedule and daily activities and interacting with friends on social media platforms, making It is feasible to leveraging the online social network data for stress detection. Data mining techniques are used for a various number of applications. In industry, data mining plays an important role in Detecting Stress In this paper, propose a new model to detect stress. In this model, initially, find a correlation of users stress states and social interactions effectively. This defines set of stress-related textual, visual, and social attributes from various aspects and proposes a novel hybrid model combined with Convolutional Neural Network CNN to leverage tweet content and social interaction information for stress detection effectively. From the experimental results, the proposed model can improve the detection performance reaches 97.8% accuracy which is faster than the existing system.