Article

Topic activation analysis for document streams based on document arrival rate and relevance

Authors:

Chunhua Cui,

Hiroyuki KitagawaAuthors Info & Claims

SAC '05: Proceedings of the 2005 ACM symposium on Applied computing

Pages 1089 - 1095

https://doi.org/10.1145/1066677.1066924

Published: 13 March 2005 Publication History

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Abstract

With the advance of network technology in recent years, the dissemination and exchange of massive documents has become commonplace. Accordingly, the importance of content analysis techniques is increasing. Topic analysis in large-scale document streams such as E-mails and news articles is an important research issue. This paper addresses techniques for "topic activation analysis" for document streams. For example, when news articles with a strong relationship to a given topic arrive frequently in a news stream, we can regard the activation level of the topic as high. In [1], Kleinberg proposed a method for analyzing document streams. Although the main objective of his method was to detect bursts of topics, it can also be used for topic activation analysis. His method, however, has a serious limitation in that it only looks at the arrival rate of documents and ignores the degree of relevance for each document. Another limitation is that his method is "batch-oriented." This paper first proposes a novel topic activation analysis scheme that incorporates both document arrival rate and relevance to address the first problem. It then presents an incremental scheme more appropriate for a document streaming environment. The proposed schemes are validated by experiments using real CNN news articles.

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Cited By

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Tu YSeng J(2018)Indices of novelty for emerging topic detectionInformation Processing and Management: an International Journal10.1016/j.ipm.2011.07.00648:2(303-325)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.ipm.2011.07.006
Khy SIshikawa YKitagawa H(2018)A Novelty-based Clustering Method for On-line DocumentsWorld Wide Web10.1007/s11280-007-0018-911:1(1-37)Online publication date: 25-Dec-2018
https://dl.acm.org/doi/10.1007/s11280-007-0018-9
Awano YMa QYoshikawa M(2014)Causal Analysis for Supporting Users' Understanding of Investment TrustsProceedings of the 16th International Conference on Information Integration and Web-based Applications & Services10.1145/2684200.2684364(524-528)Online publication date: 4-Dec-2014
https://dl.acm.org/doi/10.1145/2684200.2684364
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Index Terms

Topic activation analysis for document streams based on document arrival rate and relevance
1. Information systems
  1. Information retrieval
    1. Document representation
2. Theory of computation
  1. Semantics and reasoning
    1. Program reasoning
      1. Abstraction

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Information & Contributors

Information

Published In

SAC '05: Proceedings of the 2005 ACM symposium on Applied computing

March 2005

1814 pages

ISBN:1581139640

DOI:10.1145/1066677

Conference Chair:
Hisham M. Haddad
Kennesaw State University
,
Editor:
Lorie M. Liebrock
New Mexico Institute of Mining and Technology, Socorro, NM
,
Program Chairs:
Andrea Omicini
Alma Mater Studiorum, Universita di Bologna, Italy
,
Roger L. Wainwright
Univerity of Tulsa, OK

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 March 2005

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SAC05: The 2005 ACM Symposium on Applied Computing

March 13 - 17, 2005

New Mexico, Santa Fe

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

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Tu YSeng J(2018)Indices of novelty for emerging topic detectionInformation Processing and Management: an International Journal10.1016/j.ipm.2011.07.00648:2(303-325)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.ipm.2011.07.006
Khy SIshikawa YKitagawa H(2018)A Novelty-based Clustering Method for On-line DocumentsWorld Wide Web10.1007/s11280-007-0018-911:1(1-37)Online publication date: 25-Dec-2018
https://dl.acm.org/doi/10.1007/s11280-007-0018-9
Awano YMa QYoshikawa M(2014)Causal Analysis for Supporting Users' Understanding of Investment TrustsProceedings of the 16th International Conference on Information Integration and Web-based Applications & Services10.1145/2684200.2684364(524-528)Online publication date: 4-Dec-2014
https://dl.acm.org/doi/10.1145/2684200.2684364
Ma QHasegawa K(2014)Organizing Sightseeing Tweets Based on Content Relatedness and SharabilityWeb-Age Information Management10.1007/978-3-319-08010-9_57(510-521)Online publication date: 2014
https://doi.org/10.1007/978-3-319-08010-9_57
Hasegawa KMa QYoshikawa M(2012)Trip Tweets Search by Considering Spatio-temporal Continuity of User BehaviorDatabase and Expert Systems Applications10.1007/978-3-642-32597-7_13(141-155)Online publication date: 2012
https://doi.org/10.1007/978-3-642-32597-7_13
Gang Du Jun Guo Weiran Xu Zhen Yang (2010)Maximizing the reliability of two-state automaton for burst feature detection in news streams2010 IEEE International Conference on Progress in Informatics and Computing10.1109/PIC.2010.5687459(229-233)Online publication date: Dec-2010
https://doi.org/10.1109/PIC.2010.5687459
Tu YSeng J(2009)Research intelligence involving information retrieval - An example of conferences and journalsExpert Systems with Applications: An International Journal10.1016/j.eswa.2009.03.01536:10(12151-12166)Online publication date: 1-Dec-2009
https://dl.acm.org/doi/10.1016/j.eswa.2009.03.015
Cho YXu XHwang WZhang A(2007)Feature Extraction from Microarray Expression Data by Integration of Semantic KnowledgeProceedings of the Sixth International Conference on Machine Learning and Applications10.1109/ICMLA.2007.49(606-611)Online publication date: 13-Dec-2007
https://dl.acm.org/doi/10.1109/ICMLA.2007.49

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