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.
|Title of host publication||Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Editors||Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|State||Published - 1 Nov 2019|
|Event||Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - |
Duration: 1 Nov 2019 → …
|Name||Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Conference||Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Period||1/11/19 → …|
Bibliographical notePublisher Copyright:
© 2019 IEEE.
- internal cluster indexes
- natural language processing
- topic modeling