Volume 15, Issue 10 · IJARST · ISSN 2319-1783 / 2320-1126

Detecting Stress in Social Media Posts: A Comparative Study of Machine Learning Algorithms and GPT-Based Classification

Detecting Stress in Social Media Posts: A Comparative Study of Machine Learning Algorithms and GPT-Based Classification is an open-access, peer-reviewed research paper by Santosh Bhandari, published in Volume 15, Issue 10 of the International Journal of Advanced Research in Science and Technology (IJARST), a UGC-approved journal (Print ISSN 2319-1783, Online ISSN 2320-1126).

Author

Santosh Bhandari

Abstract

Social media platforms have become a rich source of information about users’ mental well-being, including stress. This study compares a fine-tuned GPT-2 classifier with four traditional machine learning (ML) algorithms—Naïve Bayes, Decision Tree, Logistic Regression and Random Forest—for detecting stress in tweets. The experiments used the mental-health-stress Twitter dataset from Kaggle, which contains 20,001 labeled posts. After pre-processing, the posts were classified as stressed or not stressed, and all models were evaluated using accuracy, precision, recall and F1-score. The GPT-2 classifier achieved the highest accuracy (88.7%) and an F1-score of 0.89, compared with 70–78% accuracy for the traditional algorithms. These results indicate that transformer-based language models capture the contextual cues of stress in short, informal text more effectively than frequency-based classifiers. Such models could support scalable, non-invasive screening tools for mental-health monitoring, although the study is limited by its single dataset, undocumented labelling procedure and single train–test split.

Keywords: Stress Detection, GPT-2, Sentiment Analysis, Machine Learning, Natural Language Processing, Twitter, Social Media

DOI: https://doi.org/10.62226/ijarst20262836

 

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DOI

10.62226/ijarst20262836

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How do you cite this paper?

Santosh Bhandari — “Detecting Stress in Social Media Posts: A Comparative Study of Machine Learning Algorithms and GPT-Based Classification.” International Journal of Advanced Research in Science and Technology (IJARST), Volume 15, Issue 10. DOI: 10.62226/ijarst20262836.

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