Abstract—Automatic classification of text is an increasingly important area of research. It has important applications in virtual assistants and recommender systems. Among the different types of literary works, the poem is one of the most difficult to classify automatically because of the prolific use of metaphors and the short length. In this research, we propose a data-driven approach to automatically classify Korean poems. We use three different methods for finding keywords which can train the classifiers. Our results show that the proposed approach can produce better classification accuracy than using a predefined list of keywords created by a human expert.
 

Index Terms — automatic classification, data-driven, poem, text mining, Korean text, keyword extraction

Cite: Joo Hyun Nam and Kin Choong Yow, "A Data-driven Approach to the Automatic Classification of Korean Poetry," Vol. 8, No. 3, pp. 172-176, August, 2017. doi: 10.12720/jait.8.3.172-176