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 contributionScientificpeer-review

5 Citations (Scopus)


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
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9789811005145
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventInternational Conference of the Pacific Association for Computational Linguistics - Bali, Indonesia
Duration: 19 May 201521 May 2015
Conference number: 14

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)18650929


ConferenceInternational Conference of the Pacific Association for Computational Linguistics
Abbreviated titlePACLING


  • Topic derivation
  • Tweets relationship
  • Twitter

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