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

doi:10.1109/BIBM47256.2019.8983167

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

Universidad del Pacífico

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

8 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Editores	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	1544-1547
Número de páginas	4
ISBN (versión digital)	978-172811867-3
DOI	https://doi.org/10.1109/BIBM47256.2019.8983167
Estado	Publicada - 1 nov. 2019
Evento	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - Duración: 1 nov. 2019 → …

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Nombre	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conferencia

Conferencia	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Período	1/11/19 → …

Nota bibliográfica

Funding Information:
Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA183962. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Publisher Copyright:
© 2019 IEEE.

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Lossio-Ventura, J. A., Morzan, J., Alatrista-Salas, H., Hernandez-Boussard, T., & Bian, J. (2019). Clustering and topic modeling over tweets: A comparison over a health dataset. En I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 1544-1547). Artículo 8983167 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8983167

Lossio-Ventura, Juan Antonio ; Morzan, Juandiego ; Alatrista-Salas, Hugo et al. / Clustering and topic modeling over tweets : A comparison over a health dataset. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. editor / Illhoi Yoo ; Jinbo Bi ; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1544-1547 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

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title = "Clustering and topic modeling over tweets: A comparison over a health dataset",

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.",

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Lossio-Ventura, JA, Morzan, J, Alatrista-Salas, H, Hernandez-Boussard, T & Bian, J 2019, Clustering and topic modeling over tweets: A comparison over a health dataset. En I Yoo, J Bi & XT Hu (eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019., 8983167, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1544-1547, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, 1/11/19. https://doi.org/10.1109/BIBM47256.2019.8983167

Clustering and topic modeling over tweets: A comparison over a health dataset. / Lossio-Ventura, Juan Antonio; Morzan, Juandiego; Alatrista-Salas, Hugo et al.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. ed. / Illhoi Yoo; Jinbo Bi; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1544-1547 8983167 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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Lossio-Ventura JA, Morzan J, Alatrista-Salas H, Hernandez-Boussard T, Bian J. Clustering and topic modeling over tweets: A comparison over a health dataset. En Yoo I, Bi J, Hu XT, editores, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1544-1547. 8983167. (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). doi: 10.1109/BIBM47256.2019.8983167

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

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