Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (4): 54-63.DOI: 10.3778/j.issn.1002-8331.2209-0042

• Research Hotspots and Reviews • Previous Articles     Next Articles

Review of Depression Detection Using Social Media Text Data

XU Dongdong, CAI Xiaohong, LIU Jing, CAO Hui   

  1. College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
  • Online:2023-02-15 Published:2023-02-15



  1. 山东中医药大学 智能与信息工程学院,济南 250355

Abstract: 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.

Key words: social media, text data, depression, machine learning

摘要: 近年来,机器学习被逐渐运用到基于社交媒体文本数据的抑郁症检测中并凸显重要应用价值。为梳理其应用现状和发展方向,对用于抑郁症检测的社交媒体文本数据集、数据预处理和机器学习方法进行整理分类。在数据特征表示方面,对比分析了基础特征表示、静态词嵌入和语境词嵌入。全面分析了利用不同基础特征和不同算法类型的传统机器学习以及深度学习进行抑郁症检测的性能和特点。总结并建议未来在中文数据集的创建、模型的可解释性、基于隐喻的检测和轻量级预训练模型方面做进一步的探索。

关键词: 社交媒体, 文本数据, 抑郁症, 机器学习