Psychological stress poses a current threat to the well-being of individuals. The prevalence of stress among individuals is on the rise due to the escalating pace of modern life. According to a ...global poll published by a new business in 2010, a significant increase in stress has been observed among over half of the population in the preceding two years. While stress is a ubiquitous aspect of daily life, excessive and enduring stress can have adverse effects on an individual's psychological and physiological health. The proliferation of social media platforms is exerting a significant influence on individuals' lives as well as the field of health and well-being research. The proliferation of platforms such as Twitter and Facebook has led to an increased inclination among individuals to share their daily experiences, and emotions, and engage in communication with friends via social media. This paper aims to explore the topic of stress prediction through the utilization of social networking sites. In this study, textual data is employed for the purpose of predicting stress levels. Machine learning techniques are employed for the purpose of conducting stress analysis classification. Among the models evaluated, Logistic Regression demonstrated superior performance, achieving a precision of 83%, recall of 100%, and an f1-score of 91%.
