Hashtag sense induction based on co-occurrence graphs

Mengmeng Wang, Mizuho Iwaihara

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

Abstract

Twitter hashtags are used to categorize tweets for improving search categorizing topic. But the fact that people can create and use hashtags freely leads to a situation such that one hashtag may have multiple senses. In this paper, we propose a method to induce senses of a hashtag in a particular time frame. Our assumption is that for a sense of a hashtag the context words around it are similar. Then we design a method that uses a co-occurrence graph and community detection algorithm. Both words and hashtags are nodes of the cooccurrence graph, and an edge represents the relation of two nodes co-occurring in the same tweet. A list of words with a high node degree representing a sense is extracted as a community of the graph. We take Wikipedia disambiguation list page as word sense inventory to refine the results by removing non-sense topics.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages154-165
Number of pages12
Volume9313
ISBN (Print)9783319252544
DOIs
Publication statusPublished - 2015
Event17th Asia-PacificWeb Conference, APWeb 2015 - Guangzhou, China
Duration: 2015 Sep 182015 Sep 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9313
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other17th Asia-PacificWeb Conference, APWeb 2015
CountryChina
CityGuangzhou
Period15/9/1815/9/20

Keywords

  • Co-occurrence Graph
  • Hashtag
  • Sense induction
  • Twitter
  • Wikipedia

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Wang, M., & Iwaihara, M. (2015). Hashtag sense induction based on co-occurrence graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9313, pp. 154-165). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9313). Springer Verlag. https://doi.org/10.1007/978-3-319-25255-1_13