Identifying evolutionary topic temporal patterns based on bursty phrase clustering

Yixuan Liu, Zihao Gao, Mizuho Iwaihara

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

1 Citation (Scopus)

Abstract

We discuss a temporal text mining task on finding evolutionary patterns of topics from a collection of article revisions. To reveal the evolution of topics, we propose a novel method for finding key phrases that are bursty and significant in terms of revision histories. Then we show a time series clustering method to group phrases that have similar burst histories, where additions and deletions are separately considered, and time series is abstracted by burst detection. In clustering, we use dynamic time warping to measure the distance between time sequences of phrase frequencies. Experimental results show that our method clusters phrases into groups that actually share similar bursts which can be explained by real-world events.

Original languageEnglish
Title of host publicationWeb and Big Data - 1st International Joint Conference, APWeb-WAIM 2017, Proceedings
PublisherSpringer Verlag
Pages276-284
Number of pages9
Volume10367 LNCS
ISBN (Print)9783319635637
DOIs
Publication statusPublished - 2017
Event1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017 - Beijing, China
Duration: 2017 Jul 72017 Jul 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10367 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017
CountryChina
CityBeijing
Period17/7/717/7/9

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Keywords

  • Burst detection
  • Clustering
  • DTW
  • Temporal pattern
  • Topic evolution

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Liu, Y., Gao, Z., & Iwaihara, M. (2017). Identifying evolutionary topic temporal patterns based on bursty phrase clustering. In Web and Big Data - 1st International Joint Conference, APWeb-WAIM 2017, Proceedings (Vol. 10367 LNCS, pp. 276-284). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10367 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-63564-4_22