Automatic topics discovery from hyperlinked documents

  • Authors:

  • Kuo-Jui Wu;Meng-Chang Chen;Yeali Sun

  • Affiliations:

  • Institute of Information Science, Academia Sinica, 128, Section 2, Academic Road, Nankang 115, Taipei, Taiwan;Institute of Information Science, Academia Sinica, 128, Section 2, Academic Road, Nankang 115, Taipei, Taiwan;Department of Information Management, National Taiwan University, No. 50, Lane 144, Kee-Lung Road, Section 4, Taipei, Taiwan

  • Venue:
  • Information Processing and Management: an International Journal
  • Year:
  • 2004

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Abstract

Topic discovery is an important means for marketing, e-Business and social science studies. As well, it can be applied to various purposes, such as identifying a group with certain properties and observing the emergence and diminishment of a certain cyber community. Previous topic discovery work (J.M. Kleinberg, Proceedings of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms, San Francisco, California, p. 668) requires manual judgment of usefulness of outcomes and is thus incapable of handling the explosive growth of the Internet. In this paper, we propose the Automatic Topic Discovery (ATD) method, which combines a method of base set construction, a clustering algorithm and an iterative principal eigenvector computation method to discover the topics relevant to a given query without using manual examination. Given a query, ATD returns with topics associated with the query and top representative pages for each topic. Our experiments show that the ATD method performs better than the traditional eigenvector method in terms of computation time and topic discovery quality.