Incorporating tweet relationships into topic derivation

Robertus Nugroho*, Diego Molla-Aliod, Jian Yang, Youliang Zhong, Cecile Paris, Surya Nepal

*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

With its rapid users growth, Twitter has become an essential source of information about what events are happening in the world. It is critical to have the ability to derive the topics from Twitter messages (tweets), that is, to determine and characterize the main topics of the Twitter messages (tweets). However, tweets are very short in nature and therefore the frequency of term co-occurrences is very low. The sparsity in the relationship between tweets and terms leads to a poor characterization of the topics when only the content of the tweets is used. In this paper, we exploit the relationships between tweets and propose intLDA, a variant of Latent Dirichlet Allocation (LDA) that goes beyond content and directly incorporates the relationship between tweets. We have conducted experiments on a Twitter dataset and evaluated the performance in terms of both topic coherence and tweet-topic accuracy. Our experiments show that intLDA outperforms methods that do not use relationship information.

Original languageEnglish
Title of host publicationComputational Linguistics - 14th International Conference of the Pacific Association for Computaitonal Linguistics, PACLING 2015, Revised Selected Papers
EditorsKôiti Hasida, Ayu Purwarianti
Place of PublicationSingapore
PublisherSpringer, Springer Nature
Pages177-190
Number of pages14
Volume593
ISBN (Print)9789811005145
DOIs
Publication statusPublished - 2016
Event14th International Conference of the Pacific Association for Computaitonal Linguistics, PACLING 2015 - Bali, Indonesia
Duration: 19 May 201521 May 2015

Publication series

NameCommunications in Computer and Information Science
Volume593
ISSN (Print)18650929

Other

Other14th International Conference of the Pacific Association for Computaitonal Linguistics, PACLING 2015
CountryIndonesia
CityBali
Period19/05/1521/05/15

Keywords

  • Topic derivation
  • Tweets relationship
  • Twitter

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  • Cite this

    Nugroho, R., Molla-Aliod, D., Yang, J., Zhong, Y., Paris, C., & Nepal, S. (2016). Incorporating tweet relationships into topic derivation. In K. Hasida, & A. Purwarianti (Eds.), Computational Linguistics - 14th International Conference of the Pacific Association for Computaitonal Linguistics, PACLING 2015, Revised Selected Papers (Vol. 593, pp. 177-190). (Communications in Computer and Information Science; Vol. 593). Singapore: Springer, Springer Nature. https://doi.org/10.1007/978-981-10-0515-2_13