A Comprehensive Study and Analysis of Machine Learning Algorithms Using Sentiment Analysis for Monitoring Mental Health Issues among Students

The psychological problems in students have increased as a significant issue all over the world due to excessive demand in academics, a competitive environment, social isolation, and hyper-digital exposure. Psychological distress cannot be identified at its early stage because students normally do not like to discuss their emotional difficulties openly. Digital communications are also involved since text emotion is more expressed by students' social media, learning management systems, online forums and feedback platforms. In the process of sentiment analysis, the machine learning algorithms give us a hint on how to automatically recognize the patterns of emotions in the text data. In this paper we discussed detailed research and compared different machine learning algorithms that are used in sentiment analysis for mental health issues in students. The study compares classical, kernel based, and collective learning algorithms based on multiple performance metrics. The proposed research also provides a conceptual framework for automated mental health monitoring systems at educational institutions. The results reveal that ensemble and advanced classifiers are more efficient than basic models in recognizing negative emotional states. So that early mental health support facility can be provided.