ArticleFrontiers in artificial intelligence2023
Supervised machine learning models for depression sentiment analysis.
Article in Frontiers in artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 33 citations in OpenAlex.
- Machine learning predicts diabetes risk in high-risk populations: analysis of National Health and Nutrition Examination Survey data.Archives of medical science : AMS · 2026Article
- Comparative sentiment and thematic analysis of depression-related discourse between healthcare professionals and non-healthcare individuals on Chinese social media: a computational text analysis study.Frontiers in psychology · 2026Article
- Multimodal machine learning for video based single question mental health assessment.NPJ digital medicine · 2025Article
- Mobile Mental Health Screening in EmotiZen via the Novel Brain-Inspired MCoG-LDPSNet.Biomimetics (Basel, Switzerland) · 2025Article
- A hybrid ECC-AES encryption framework for secure and efficient cloud-based data protection.Scientific reports · 2025Article
- BERT and BERTopic for screening clinical depression on open-ended text messages collected through a mobile application from older adults.BMC public health · 2025Article
- Patient Voices in Dialysis Care: Sentiment Analysis and Topic Modeling Study of Social Media Discourse.Journal of medical Internet research · 2025Article
- Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study.PloS one · 2025Article
- Predicting depression among men who have sex with men in Ghana using machine learning algorithms.PLOS mental health · 2025Article
- Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU.Frontiers in public health · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Globally, the prevalence of mental health problems, especially depression, is at an all-time high. The objective of this study is to utilize machine learning models and sentiment analysis techniques to predict the level of depression earlier in social media users' posts. Methods: The datasets used in this research were obtained from Twitter posts. Four machine learning models, namely extreme gradient boost (XGB) Classifier, Random Forest, Logistic Regression, and support vector machine (SVM), were employed for the prediction task. Results: The SVM and Logistic Regression models yielded the most accurate results when applied to the provided datasets. However, the Logistic Regression model exhibited a slightly higher level of accuracy compared to SVM. Importantly, the logistic regression model demonstrated the advantage of requiring less execution time. Discussion: The findings of this study highlight the potential of utilizing machine learning models and sentiment analysis techniques for early detection of depression in social media users. The effectiveness of SVM and Logistic Regression models, with Logistic Regression being more efficient in terms of execution time, suggests their suitability for practical implementation in real-world scenarios.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.