Fast Identification of Topic Burst Patterns Based on Temporal Clustering

Zhuoyang Xu, Mizuho Iwaihara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Temporal text mining is widely used in summarization and tracking of evolutionary topic trends. In online collaborative systems like Wikipedia, edit history of each article is stored as revisions. Topics of articles or categories grow and fade over time and retain evolutionary information in edit history. This paper studies a particular temporal text mining task: quickly finding burst patterns of topics from phrases extracted from edit history of Wikipedia articles. We first extract several candidate phrases from edit history by specific features and build time series with edit frequency. Temporal clustering of burst patterns of phrases reveals bursts of topics. However, distance measure for temporal clustering, such as dynamic time warping (DTW), is often costly. In this paper, we propose segmented DTW which decomposes time series into proper segments and computes DTW distance within segments separately. Our segmented DTW shows reasonable speed up over DTW, while the proposed method can identify interesting evolutionary topic burst patterns effectively. Research so far can be applied in domains like trend tracking, temporal relatedness of phrases and popular topic discovery.

Original languageEnglish
Title of host publicationProceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages548-553
Number of pages6
ISBN (Electronic)9781538674475
DOIs
Publication statusPublished - 2019 Apr 16
Event7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018 - Yonago, Japan
Duration: 2018 Jul 82018 Jul 13

Publication series

NameProceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018

Conference

Conference7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018
CountryJapan
CityYonago
Period18/7/818/7/13

Keywords

  • Burst detection
  • Dynamic time warp
  • Temporal clustering
  • Topic evolution

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Communication
  • Information Systems
  • Information Systems and Management
  • Education

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