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

Laura Anderlucci () and Cinzia Viroli ()
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Laura Anderlucci: University of Bologna
Cinzia Viroli: University of Bologna

Advances in Data Analysis and Classification, 2020, vol. 14, issue 4, No 2, 759-770

Abstract: 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.

Keywords: Clustering; Gradient descent algorithm; Mixture models; Text data analysis; 62H30 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11634-020-00399-3

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