Keyword Data Analysis using Bayesian Regression Model with Regularization

Q: What does this research mean for the field?

A Bayesian regularized regression model using a negative binomial distribution and a Horseshoe prior provides superior predictive performance for keyword appearance frequency in high-dimensional, sparse, and overdispersed document-keyword matrices compared to existing count data regression models. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Sunghae Jun

doi:10.5391/jkiis.2026.36.4.387

August 31, 2026Journal of Korean institute of intelligent systems

Keyword Data Analysis using Bayesian Regression Model with Regularization

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Key Points

To develop a Bayesian regularized count regression model that addresses high sparsity, overdispersion, multicollinearity, and overfitting in document-keyword data.
Constructed a Bayesian regression framework combining a negative binomial distribution with a Horseshoe prior for simultaneous coefficient shrinkage and variable selection.
Applied correlation-based feature selection and Hamiltonian Monte Carlo algorithms for posterior parameter estimation and keyword frequency forecasting.
Evaluated model performance using a document-keyword matrix derived from synthetic data technology patents, designating 'synthetic' as the target count variable.
The proposed model demonstrated lower mean squared error and mean absolute error compared to conventional count regression models.
The framework demonstrated effective handling of extreme sparsity and overdispersion in high-dimensional text matrices without overfitting.

Implication

Methodological study demonstrates improved frequency prediction in patent document keywords, highlighting the utility of Bayesian regularized count regression.

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Cite This Study

Sunghae Jun (2026) studied this question.

synapsesocial.com/papers/6a99355b636c6408cfa7d8b8 https://doi.org/10.5391/jkiis.2026.36.4.387

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Keyword Data Analysis using Bayesian Regression Model with Regularization

Key Points

Implication

Abstract

Discussion

Cite This Study

Keyword Data Analysis using Bayesian Regression Model with Regularization

Key Points

Implication

Abstract

Discussion

Cite This Study

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