거시적 이슈 트래킹의 한계 극복을 위한 개인 관심 트래킹 방법론

• 영문명: Individual Interests Tracking : Beyond Macro-level Issue Tracking
• 발행기관: 한국IT서비스학회
• 저자명: 류신(Chen Liu) 김남규(Nam gyu Kim)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제13권 제4호, 275~287쪽, 전체 13쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2014.12.31

영문 초록

Recently, the volume of unstructured text data generated by various social media has been increasing rapidly; consequently, the use of text mining to support decision-making has also been growing. In particular, academia and industry are paying significant attention to topic analysis in order to discover the main issues from a large volume of text documents. Topic analysis can be regarded as static analysis because it analyzes a snapshot of the distribution of various issues. In contrast, some recent studies have attempted to perform dynamic issue tracking, which analyzes and traces issue trends during a predefined period. However, most traditional issue tracking methods have a common limitation : when a new period is included, topic analysis must be repeated for all the documents of the entire period, rather than being conducted only on the new documents of the added period. Additionally, traditional issue tracking methods do not concentrate on the transition of individuals' interests from certain issues to others, although the methods can illustrate macro-level issue trends. In this paper, we propose an individual interests tracking methodology to overcome the two limitations of traditional issue tracking methods. Our main goal is not to track macro-level issue trends but to analyze trends of individual interests flow. Further, our methodology has extensible characteristics because it analyzes only newly added documents when the period of analysis is extended. In this paper, we also analyze the results of applying our methodology to news articles and their access logs.

목차

Abstract

1. 서론

2. 관련 연구

3. 개인 관심 트래킹 방법론

4. 실험

5. 결론

References

키워드

Interests Tracking Issue Tracking Text Mining Topic Analysis