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

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Title: Unraveling the inner world of PhD scholars with sentiment analysis for mental health prognosis.
Authors: Noreen, Rimsha, Zafar, Amna, Waheed, Talha, Wasim, Muhammad, Ahad, Abdul, Coelho, Paulo Jorge, Pires, Ivan Miguel
Source: Behaviour & Information Technology. Jun2025, Vol. 44 Issue 10, p2244-2256. 13p.
Subjects: Social media, Random forest algorithms, Research funding, Mental health, Doctoral programs, Mental illness, Anxiety, Emotions, Natural language processing, Surveys, Motivation (Psychology), Support vector machines, Artificial neural networks, Sentiment analysis, Machine learning, Mental depression, Algorithms, Evaluation
Geographic Terms: Pakistan
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 (). [ABSTRACT FROM AUTHOR]
Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: 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 (). [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/0144929X.2023.2289057
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 2244
    Subjects:
      – SubjectFull: Social media
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Mental health
        Type: general
      – SubjectFull: Doctoral programs
        Type: general
      – SubjectFull: Mental illness
        Type: general
      – SubjectFull: Anxiety
        Type: general
      – SubjectFull: Emotions
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Surveys
        Type: general
      – SubjectFull: Motivation (Psychology)
        Type: general
      – SubjectFull: Support vector machines
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Sentiment analysis
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Mental depression
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Evaluation
        Type: general
      – SubjectFull: Pakistan
        Type: general
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      – TitleFull: Unraveling the inner world of PhD scholars with sentiment analysis for mental health prognosis.
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              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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