Sentiment analysis that classifies data into positive or negative has been dominantly used to recognize emotional aspects of texts, despite the deficit of thorough examination of emotional meanings. Recently, corpora labeled with more than just valence are built to exceed this limit. However, most Korean emotion corpora are small in the number of instances and cover a limited range of emotions. We introduce KOTE dataset. KOTE contains 50k (250k cases) Korean online comments, each of which is manually labeled for 43 emotion labels or one special label (NO EMOTION) by crowdsourcing (Ps = 3,048). The emotion taxonomy of the 43 emotions is systematically established by cluster analysis of Korean emotion concepts expressed on word embedding space. After explaining how KOTE is developed, we also discuss the results of finetuning and analysis for social discrimination in the corpus.
翻译：将数据归为正或负的感官分析被主要用于承认文本的情感方面,尽管缺乏对情感含义的彻底检查。最近,为超过这一限度,建立了不仅价值高而且贴有标签的公司。然而,大多数韩国情感公司的数量很少,涵盖有限的情感。我们引入KOTE数据集。KOTE包含50k (250k 个案例)韩国在线评论,每份都手工标有43个情感标签或由众包(Ps = 3,048)的一个特殊标签(NO EMOTION)的标签(Ps = 3,048),这43种情感的情感分类系统化是通过在嵌入空间的文字上表达的韩国情感概念的集束分析而建立的。在解释KOTE是如何发展的之后,我们还讨论对材料中的社会歧视进行微调和分析的结果。
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