Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Dynamic Text Categorizing Method using Text Mining and Association Rule

  • Kim, Young-Wook (School of Industrial and Management Engineering, Korea University) ;
  • Kim, Ki-Hyun (School of Industrial and Management Engineering, Korea University) ;
  • Lee, Hong-Chul (School of Industrial and Management Engineering, Korea University)
  • Received : 2017.11.20
  • Accepted : 2017.12.02
  • Published : 2018.10.31
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Abstract

In this paper, we propose a dynamic document classification method which breaks away from existing document classification method with artificial categorization rules focusing on suppliers and has changing categorization rules according to users' needs or social trends. The core of this dynamic document classification method lies in the fact that it creates classification criteria real-time by using topic modeling techniques without standardized category rules, which does not force users to use unnecessary frames. In addition, it can also search the details through the relevance analysis by calculating the relationship between the words that is difficult to grasp by word frequency alone. Rather than for logical and systematic documents, this method proposed can be used more effectively for situation analysis and retrieving information of unstructured data which do not fit the category of existing classification such as VOC (Voice Of Customer), SNS and customer reviews of Internet shopping malls and it can react to users' needs flexibly. In addition, it has no process of selecting the classification rules by the suppliers and in case there is a misclassification, it requires no manual work, which reduces unnecessary workload.

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