2021
DOI: 10.1007/978-3-030-86331-9_23
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Sparse Document Analysis Using Beta-Liouville Naive Bayes with Vocabulary Knowledge

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Cited by 2 publications
(1 citation statement)
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“…In this paper, we introduce two novel models, GDMPCA and BLMPCA, that significantly improve text classification and sentiment analysis by combining generalized Dirichlet (GD) and Beta-Liouville (BL) distributions for a more in-depth understanding of text data complexities [16,24,25]. Both models employ variational Bayesian inference and collapsed Gibbs sampling for efficient and scalable computational performances, which is critical for handling large datasets.…”
Section: Introductionmentioning
confidence: 99%
“…In this paper, we introduce two novel models, GDMPCA and BLMPCA, that significantly improve text classification and sentiment analysis by combining generalized Dirichlet (GD) and Beta-Liouville (BL) distributions for a more in-depth understanding of text data complexities [16,24,25]. Both models employ variational Bayesian inference and collapsed Gibbs sampling for efficient and scalable computational performances, which is critical for handling large datasets.…”
Section: Introductionmentioning
confidence: 99%