Unraveling the inner world of PhD scholars with sentiment analysis for mental health prognosis

Abstract

ABSTRACT Mental health challenges among PhD scholars are a growing global concern, with a survey in the UK revealing that at least 86% of students face depression and anxiety. Social media platforms offer valuable insights into the depression levels of PhD students. Sentiment analysis for social media content can help identify indicators of anxiety, such as negative language, stress expressions, or mental health struggles. This paper uses social media and surveys to develop a dataset for Pakistani graduate students. The dataset collects 5096 social media posts from 1170 users, categorising them into anxiety (46.7%), depression (12.6%), and motivation (40.7%) based on mental health levels. The survey responses are combined with the social media dataset. Machine learning models, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF), are used to detect the mental health status of PhD scholars. The study finds that 59.3% of graduate students in Pakistan face anxiety and mental health issues, indicating a need for policy reformulation in graduate programmes. The research data is available online for further research (https://github.com/dr-m-wasim/PhD-Scholars-Mental-Health).