초록

Since a decade, Text-Driven Emotion Processing has improved greatly, and is widely adopted in Opinion Mining or Sentiment Analysis. Based on coarse-grained classification, these sub-fields use only a couple of emotion features (2∼6) so far, and need to enlarge their success to more fined-grained emotion processing. In order to apply (semi-)automatic processing, the prerequisite is the construction of an enriched ontology in quality and quantity for every target language. This study reports in detail the construction of KoFiGEmOnto, a Korean Fine-Grained Emotion Ontology, for this purpose. In our previous studies, we have conducted component analysis of emotions and sophisticate criteria were dressed for building emotion ontology. Our raw corpus (about 22,000 utterances) was composed of 3 different kinds of texts, i.e. emojis, SMS texts, and film scripts.After careful analysis according to the proposed criteria, the first version of KoFiGEmOnto (approximately 11,300 unique types of emotional expressions) was constructed. We expect that KoFiGEmOnto will serve as a seed ontology for Korean fine-grained emotion processing.

키워드

감성 분석, 세분화된 감정명세, 플러칙, 한국어 감정 온톨로지, 텍스트 기반 감정 처리, 코피게몬토

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