EmoMix+:一种基于情感词典的移动应用抑郁检测方法

Ran Li, Yuanfei Zhang, Lihua Yin, Zhe Sun, Zheng Lin, Peng Fu, Weiping Wang, Gang Shi
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引用次数: 2

摘要

情感词汇是文本情感分析的重要辅助资源。以往的工作主要集中在积极和消极的分类上,对细粒度的情感分类研究较少。研究人员使用基于词汇的方法发现,抑郁症患者在社交媒体上表达更多的负面情绪。情绪特征是检测抑郁症的有效特征,但传统的情绪词汇在检测抑郁症方面存在局限性,忽略了许多抑郁症词汇。因此,我们构建抑郁症情绪词汇库,进一步研究健康用户与抑郁症患者之间的差异。实验结果表明,本文构建的抑郁症词典是有效的,对抑郁症用户有较好的分类效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EmoMix+: An Approach of Depression Detection Based on Emotion Lexicon for Mobile Application
Emotion lexicon is an important auxiliary resource for text emotion analysis. Previous works mainly focused on positive and negative classification and less on fine-grained emotion classification. Researchers use lexicon-based methods to find that patients with depression express more negative emotions on social media. Emotional characteristics are an effective feature in detecting depression, but the traditional emotion lexicon has limitations in detecting depression and ignores many depression words. Therefore, we build an emotion lexicon for depression to further study the differences between healthy users and patients with depression. The experimental results show that the depression lexicon constructed in this paper is effective and has a better effect of classifying users with depression.
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