Utilizing Artificial Intelligence for the Identification of Students with Depression and Anxiety through Social Media Analysis

In response to the escalating concerns about the mental well-being of students, this research represents a significant stride in utilizing artificial intelligence (AI) to discern individuals experiencing depression and anxiety through their social media comments.

In response to the escalating concerns about the mental well-being of students, this research represents a significant stride in utilizing artificial intelligence (AI) to discern individuals experiencing depression and anxiety through their social media comments. The study employs a comprehensive suite of regression methods, including Logistic Regression, Decision Trees, Support Vector Classifier (SVC), Random Forest, and the Ridge model. The dataset, meticulously curated with 6982 comments sourced from Kaggle, undergoes processing, incorporating a conscientious split into training and testing sets for robust evaluation. The AI models exhibit remarkable performance metrics, with precision reaching an impressive 99%, F1 scores achieving 99% for normal and 95% for depressive comments, and accuracy and weighted averages standing at 99%. The study serves its immediate purpose of identifying students on social media and hints at broader applications across diverse contexts. Beyond specific demographics, the research underscores the potential of AI in addressing mental health challenges, offering insights that extend far beyond the confines of student populations, thereby contributing to the broader discourse of AI's impact on mental health.

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