Clustering and topic modeling over tweets: A comparison over a health dataset
Juan Antonio Lossio-Venturaa, Juandiego Morzánb, Hugo Alatrista-Salasb, Tina Hernandez‐Boussarda, Jiang Bianc
- aDepartment of Medicine, Biomedical Informatics, Stanford University, USA
- bSchool of Engineering, Universidad del Pacífico, Lima, Peru
- cHealth Outcomes & Biomedical Informatics, University of Florida, USA
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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.
TL;DRAn evaluation based on internal indexes of different topic modeling and document clustering applications over two Twitter health-related datasets shows that Online Twitter LDA and Gibbs LDA get a better performance for extracting topics and grouping tweets. (Semantic Scholar)