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Mixtures of Dirichlet-Multinomial distributions for supervised and unsupervised classification of short text data

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  • Laura Anderlucci

    (University of Bologna)

  • Cinzia Viroli

    (University of Bologna)

Abstract

Topic detection in short textual data is a challenging task due to its representation as high-dimensional and extremely sparse document-term matrix. In this paper we focus on the problem of classifying textual data on the base of their (unique) topic. For unsupervised classification, a popular approach called Mixture of Unigrams consists in considering a mixture of multinomial distributions over the word counts, each component corresponding to a different topic. The multinomial distribution can be easily extended by a Dirichlet prior to the compound mixtures of Dirichlet-Multinomial distributions, which is preferable for sparse data. We propose a gradient descent estimation method for fitting the model, and investigate supervised and unsupervised classification performance on real empirical problems.

Suggested Citation

  • Laura Anderlucci & Cinzia Viroli, 2020. "Mixtures of Dirichlet-Multinomial distributions for supervised and unsupervised classification of short text data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 759-770, December.
    • Handle: RePEc:spr:advdac:v:14:y:2020:i:4:d:10.1007_s11634-020-00399-3
    DOI: 10.1007/s11634-020-00399-3
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    References listed on IDEAS

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    1. Ian Holmes & Keith Harris & Christopher Quince, 2012. "Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-15, February.
    2. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
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    Cited by:

    1. Zhao, Xin & Zhang, Jingru & Lin, Wei, 2023. "Clustering multivariate count data via Dirichlet-multinomial network fusion," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    2. Angela Maria D’Uggento & Albino Biafora & Fabio Manca & Claudia Marin & Massimo Bilancia, 2023. "A text data mining approach to the study of emotions triggered by new advertising formats during the COVID-19 pandemic," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2303-2325, June.

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