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Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data
Topic models are a useful and popular method to find latent topics of documents. However, the short and sparse texts in social media micro-blogs such as Twitter are challenging for the most commonly used Latent Dirichlet Allocation (LDA) topic model. We compare the performance of the standard LDA to...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer Berlin Heidelberg
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10060035/ https://www.ncbi.nlm.nih.gov/pubmed/37223721 http://dx.doi.org/10.1007/s00180-022-01246-z |