Clustering and topic modeling over tweets: A comparison over a health dataset

Juan Antonio Lossio-Ventura, Juandiego Morzan, Hugo Alatrista-Salas, Tina Hernandez-Boussard, Jiang Bian

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Twitter became the most popular form of social interactions in the healthcare domain. Thus, various teams have evaluated Twitter as an additional source where patients share information about their healthcare with the potential goal to improve their outcomes. Several existing topic modeling and document clustering applications have been adapted to assess tweets showing that the performances of the applications are negatively affected due to the nature and characteristics of tweets. Moreover, Twitter health research has become difficult to measure because of the absence of comparisons between the existing applications. In this paper, we perform an evaluation based on internal indexes of different topic modeling and document clustering applications over two Twitter health-related datasets. Our results show that Online Twitter LDA and Gibbs LDA get a better performance for extracting topics and grouping tweets. We want to provide health practitioners this comparison to select the most suitable application for their tasks.
Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
EditorsIllhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1544-1547
Number of pages4
ISBN (Electronic)978-172811867-3
DOIs
StatePublished - 1 Nov 2019
EventProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 -
Duration: 1 Nov 2019 → …

Publication series

NameProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

ConferenceProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Period1/11/19 → …

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Twitter
  • clustering
  • internal cluster indexes
  • natural language processing
  • topic modeling

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