Automatic labelling of topic models learned from Twitter by summarisation

Elizabeth Cano, Yulan He

Research output: Chapter in Book/Published conference outputConference publication


Latent topics derived by topic models such as Latent Dirichlet Allocation (LDA) are the result of hidden thematic structures which provide further insights into the data. The automatic labelling of such topics derived from social media poses however new challenges since topics may characterise novel events happening in the real world. Existing automatic topic labelling approaches which depend on external knowledge sources become less applicable here since relevant articles/concepts of the extracted topics may not exist in external sources. In this paper we propose to address the problem of automatic labelling of latent topics learned from Twitter as a summarisation problem. We introduce a framework which apply summarisation algorithms to generate topic labels. These algorithms are independent of external sources and only rely on the identification of dominant terms in documents related to the latent topic. We compare the efficiency of existing state of the art summarisation algorithms. Our results suggest that summarisation algorithms generate better topic labels which capture event-related context compared to the top-n terms returned by LDA.

Original languageEnglish
Title of host publication52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference
PublisherAssociation for Computational Linguistics
Number of pages7
ISBN (Print)978-1-937284-73-2
Publication statusPublished - 2014
Event52nd annual meeting of the Association for Computational Linguistics - Baltimore, MD, United States
Duration: 22 Jun 201427 Jun 2014


Meeting52nd annual meeting of the Association for Computational Linguistics
Abbreviated titleACL 2014
Country/TerritoryUnited States
CityBaltimore, MD


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