Review of Depression Detection Using Social Media Text Data

被引：0
|
作者：
Xu, Dongdong [1 ]
Cai, Xiaohong [1 ]
Liu, Jing [1 ]
Cao, Hui [1 ]
机构：
[1] College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan,250355, China
来源：
Computer Engineering and Applications | 2024年 / 59卷 / 04期
关键词：
Learning systems - Social networking (online);
D O I：
10.3778/j.issn.1002-8331.2209-0042
中图分类号：
学科分类号：
摘要：
Machine learning has been gradually applied to depression detection using social media text data, and has prominently shown important application value in recent years. Firstly, this paper organizes and classifies social media text datasets, data preprocessing and machine learning methods used for depression detection. In addition, in terms of data feature representation, the basic feature representation, static and contextual word embedding are compared and analyzed. Secondly, this paper analyzes comprehensively the performance and characteristics of traditional machine learning with different basic features and different algorithm types as well as deep learning for depression detection. Finally, this paper summarizes and suggests further explorations in Chinese dataset creation, model interpretability, metaphor-based detection and lightweight pre-training model. © 2024 Chinese Journal of Animal Science and Veterinary Medicine Co., Ltd.. All rights reserved.
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页码：54 / 63
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