Despite the lack of comprehensive exploration of emotional connotations, sentiment analysis, which categorizes data as positive or negative, has been widely employed to identify emotional aspects in texts. Recently, corpora labeled with more than just valence or polarity have been built to surpass this limitation. However, most Korean emotion corpora are limited by their small size and narrow range of emotions covered. In this paper, we introduce the KOTE dataset. The KOTE dataset comprises 50,000 Korean online comments, totaling 250,000 cases, each manually labeled for 43 emotions and NO EMOTION through crowdsourcing. The taxonomy for the 43 emotions was systematically derived through cluster analysis of Korean emotion concepts within the word embedding space. After detailing the development of KOTE, we further discuss the results of fine-tuning, as well as analysis for social discrimination within the corpus.