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

Bibliographic Data

ID21452372
AuthorsRimsha Noreen (University of Engineering and Technology), Amna Zafar (0000-0003-2010-3018, University of Engineering and Technology), Talha Waheed (0000-0002-8350-5914, University of Engineering and Technology), Muhammad Wasim (0000-0003-0016-7903, University of Management and Technology), Abdul Ahad (0000-0002-9496-5991, Northwestern Polytechnical University), Paulo Jorge Coelho (0000-0002-4383-0472, Polytechnic of Leiria), Ivan Miguel Pires (0000-0002-3394-6762, University of Aveiro, corresponding author)
Year2025
Volume44
Issue10
Pages2244-2256
Publication date2025-06-15
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueBehaviour and Information Technology (JOURNAL)
Journal identifiersISSN: 0144-929X • E-ISSN: 1362-3001
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/0144929x.2023.2289057
OpenAlexW4389205683
LanguageEN
Citations received2
References cited43

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)

Anxiety · Depression (economics) · Face (sociological concept) · Medical education · Mental health · Political science · Psychiatry · Public relations · Sentiment analysis · Social media · Social science · Sociology · World Wide Web · Applied Psychology · Artificial Intelligence · Cardiac Health and Mental Health · Computer Science · Medicine · Mental Health via Writing · Psychology · Sentiment Analysis and Opinion Mining

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