Abstract
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.
Highlights
Mobile applications use natural language processing (NLP) technology to extract relevant signals from user search queries or other natural language interactions
I’m a worthless person, my work is very unsuccessful.” e emotion detected from the sentiment lexicon is negative, while from the emotion lexicon, we can find that the user is very pessimistic and guilty
(ii) We use the lexicon proposed to capture the differences between depressed patients and healthy users in emotional expression, which can be used as a tool for depression expression analysis
Summary
Mobile applications use natural language processing (NLP) technology to extract relevant signals from user search queries or other natural language interactions. If the analysis of social media can detect a user with a high depression score, the government can provide relevant support and treatment in advance. The compound emotion “anxiety” is his real feeling, which is the major symptom of depression They confused emotional expression with emotional description and applied the words written to describe a person’s emotions directly to the online environment, which is different from the words people use to express their emotions on social media. (ii) We use the lexicon proposed to capture the differences between depressed patients and healthy users in emotional expression, which can be used as a tool for depression expression analysis.
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