User Guide for KOTE: Korean Online Comments Emotions Dataset
This addresses the problem of limited and small-scale Korean emotion corpora for researchers in natural language processing, though it is incremental as it builds on existing emotion taxonomy efforts.
The authors tackled the lack of comprehensive emotion datasets for Korean by introducing KOTE, a dataset of 50k Korean online comments manually labeled with 43 emotion categories, resulting in 250k cases annotated via crowdsourcing.
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.