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...

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Detalles Bibliográficos
Autores principales: Weisser, Christoph, Gerloff, Christoph, Thielmann, Anton, Python, Andre, Reuter, Arik, Kneib, Thomas, Säfken, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Original Paper
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

Internet

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

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