ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2019
Clustering and topic modeling over tweets: A comparison over a health dataset.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Decoding the language of first impressions: Comparing models of first impressions of faces derived from free-text descriptions and trait ratings.British journal of psychology (London, England : 1953) · 2026Article
- A topic modeling approach for analyzing and categorizing electronic healthcare documents in Afaan Oromo without label information.Scientific reports · 2024Article
- An integrated clustering and BERT framework for improved topic modeling.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2023Article
- Evaluation of clustering and topic modeling methods over health-related tweets and emails.Artificial intelligence in medicine · 2021Article
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Authors and funding
5 authors.
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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.
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