Sparse Document Analysis Using Beta-Liouville Naive Bayes with Vocabulary Knowledge

Najar, Fatma; Bouguila, Nizar

doi:10.1007/978-3-030-86331-9_23

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%

Hidden Variable Models in Text Classification and Sentiment Analysis

Koochemeshkian,

Ihou Koffi,

Bouguila

2024

Electronics

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In this paper, we are proposing extensions to the multinomial principal component analysis (MPCA) framework, which is a Dirichlet (Dir)-based model widely used in text document analysis. The MPCA is a discrete analogue to the standard PCA (it operates on continuous data using Gaussian distributions). With the extensive use of count data in modeling nowadays, the current limitations of the Dir prior (independent assumption within its components and very restricted covariance structure) tend to prevent efficient processing. As a result, we are proposing some alternatives with flexible priors such as generalized Dirichlet (GD) and Beta-Liouville (BL), leading to GDMPCA and BLMPCA models, respectively. Besides using these priors as they generalize the Dir, importantly, we also implement a deterministic method that uses variational Bayesian inference for the fast convergence of the proposed algorithms. Additionally, we use collapsed Gibbs sampling to estimate the model parameters, providing a computationally efficient method for inference. These two variational models offer higher flexibility while assigning each observation to a distinct cluster. We create several multitopic models and evaluate their strengths and weaknesses using real-world applications such as text classification and sentiment analysis.

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Section: Introductionmentioning

confidence: 99%