지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2014.1
- 수록면
- 275 - 287 (13page)
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초록· 키워드
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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.
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참고문헌 (19)
참고문헌 신청
Aggarwal, A. / 2014 / Mining Issue Tracking Systems Using Topic Models for Trend Analysis, Corpus Exploration, and Understanding Evolution / Proceedings of the 3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering :52~58
Albright, R. / 2006 / Taming Text with the SVD / SAS Institute Inc
Bae, J. / 2014 / Twitter Issue Tracking System by Topic Modeling Techniques / Journal of Intelligence and Information Systems 20(2):109~122
Bae, J. / 2013 / Analysis of Twitter for 2012 South Korea Presidential Election by Text Mining Techniques / Journal of Intelligence and Information Systems 19(3):141~156
Ding, W. / 2014 / Dynamic Topic Detection and Tracking：A Comparison of HDP, Cword, and Cocitation Methods / Journal of the Association for Information Science and Technology 65(10):2084~2097
Albright, R. / 2006 / Taming Text with the SVD / SAS Institute Inc
Bae, J. / 2014 / Twitter Issue Tracking System by Topic Modeling Techniques / Journal of Intelligence and Information Systems 20(2):109~122
Bae, J. / 2013 / Analysis of Twitter for 2012 South Korea Presidential Election by Text Mining Techniques / Journal of Intelligence and Information Systems 19(3):141~156
Ding, W. / 2014 / Dynamic Topic Detection and Tracking：A Comparison of HDP, Cword, and Cocitation Methods / Journal of the Association for Information Science and Technology 65(10):2084~2097
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