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Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data.

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Title: Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data.
Authors: Weisser, Christoph1,2 (AUTHOR), Gerloff, Christoph1 (AUTHOR), Thielmann, Anton1 (AUTHOR) anton.thielmann@uni-goettingen.de, Python, Andre3 (AUTHOR), Reuter, Arik1 (AUTHOR), Kneib, Thomas1,2 (AUTHOR), Säfken, Benjamin4 (AUTHOR)
Source: Computational Statistics. Jun2023, Vol. 38 Issue 2, p647-674. 28p.
Database: Mathematics Source
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  Data: Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data.
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  Data: <searchLink fieldCode="JN" term="%22Computational+Statistics%22">Computational Statistics</searchLink>. Jun2023, Vol. 38 Issue 2, p647-674. 28p.
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        Value: 10.1007/s00180-022-01246-z
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        Text: English
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      – TitleFull: Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data.
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            – D: 01
              M: 06
              Text: Jun2023
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              Y: 2023
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