The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
A dirichlet multinomial mixture model-based approach for short text clustering
Abstract
Short text clustering has become an increasingly important task with the popularity of social media like Twitter, Google+, and Facebook. It is a challenging problem due to its sparse, high-dimensional, and large-volume characteristics. In this paper, we proposed a collapsed Gibbs Sampling algorithm for the Dirichlet Multinomial Mixture model for short text clustering (abbr. to GSDMM). We found that GSDMM can infer the number of clusters automatically with a good balance between the completeness and homogeneity of the clustering results, and is fast to converge. GSDMM can also cope with the sparse and high-dimensional problem of short texts, and can obtain the representative words of each cluster. Our extensive experimental study shows that GSDMM can achieve significantly better performance than three other clustering models.
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Cited by
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More Accounts, Fewer Links: How Algorithmic Curation Impacts Media Exposure in Twitter Timelines
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Alsoubai, A., Song, J., Razi, A., Naher, N., De Choudhury, M., & Wisniewski, P. J. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555136 · Cited by 36
Metadata
- Authors
- Yin, J., & Wang, J.
- Venue
- Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2014-08-24
- Pages
- 233-242
- OpenAlex
- W2061922307
- Cited by
- 561
- References
- 30
- FWCI
- 14.63
- Type
- conference paper
- Language
- en
- Open access
- closed
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