A Comparative Study of Machine Learning Models for Stress Detection in Academic Social Media Posts
Summary
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This study evaluates machine learning approaches for stress detection in academic social media text using the Dreaddit dataset. Five classifiers were assessed across multiple feature configurations. A weighted ensemble with VADER sentiment features achieved 77.90% accuracy and 79.48% F1-Score on 715 test posts.
Fields of Study & Subjects
Mental Health via Writing
Sentiment Analysis and Opinion Mining
Emotion and Mood Recognition
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 16 March 2026
- Language
- EN