监督机器学习模型用于抑郁情绪分析
Ibidun Christiana Obagbuwa1, Samantha Danster1, Onil Colin Chibaya1
1Department of Computer Science and Information Technology, School of Natural and Applied Sciences, Sol Plaatje University, Kimberley, South Africa.
Frontiers in artificial intelligence
|August 4, 2023
概括
机器学习和情绪分析可以从社交媒体帖子中预测抑郁症. 后勤回归和支持向量机器模型显示高精度,后勤回归在早期抑郁症检测中更有效.
科学领域:
- 计算精神病学是一种计算精神病学.
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 全球精神健康问题的增加,特别是抑郁症.
- 需要早期发现抑郁症的方法.
- 社交媒体作为心理健康监测的潜在数据来源.
研究的目的:
- 在社交媒体用户的帖子中预测抑郁症水平.
- 评估用于早期抑郁症检测的机器学习模型.
- 将情绪分析技术应用于社交媒体数据.
主要方法:
- 使用Twitter帖子作为数据集.
- 采用了四种机器学习模型:极端梯度提升 (XGB) 分类器,随机森林,物流回归和支持向量机器 (SVM).
- 对抑郁症预测的模型性能进行比较.
主要成果:
- 支持矢量机 (SVM) 和物流回归模型显示了最高的准确性.
- 与SVM相比,物流回归实现了略高的精度.
- 后勤回归需要更短的执行时间,这表明效率更高.
结论:
- 机器学习和情绪分析对于在社交媒体上早期检测抑郁症是有效的.
- SVM和物流回归模型显示出实际实施的希望.
- 后勤回归为现实世界抑郁查提供了一个有效的解决方案.
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