SentiSense: Pioneering Mental Health Application Through Advanced Technology

Identifying the factors that contribute to mental health related problems: stress, depression, anxiety, and developing effective therapeutic strategies are essential. Five algorithms(Support Vector Machine, Naive Bayes, Logistic Regression, Decision Tree & Neural Network) will be used to classify students into distinct mental health categories based on data obtained from students at a higher education institution. In order to improve student mental health assistance and targeted interventions, the study intends to: Develop precise models for categorizing the mental health conditions of students; Determine the best algorithm for each category (stress, depression, and anxiety); and Provide data-driven insights. This research offers institutions evidence-based strategies for addressing mental health issues, which has the potential to greatly increase student well-being and academic success.