Supervised machine learning models for depression sentiment analysis

Frontiers in Artificial Intelligence

Artificial Intelligence

Samantha Danster¹  Onil Colin Chibaya¹  Ibidun Christiana Obagbuwa² 

[1] Department of Computer Science and Information Technology, School of Natural and Applied Sciences, Sol Plaatje University, Kimberley, South Africa;null;

关键词: Twitter;    depression;    sentiment analysis;    text pre-processing;    machine learning techniques;    social media;    natural language processing;    mental health;   

DOI  :  10.3389/frai.2023.1230649

 received in 2023-05-29, accepted in 2023-06-29,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionGlobally, 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.MethodsThe 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.ResultsThe 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.DiscussionThe 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.

【 授权许可】

Unknown   
Copyright © 2023 Obagbuwa, Danster and Chibaya.