Text Categorization Based on Topic Model

DOI

10.2991/ijcis.2009.2.4.8How to use a DOI?

Keywords

Topic model, Latent Dirichlet allocation, Variational Inference, Category LanguageModel.

Abstract

In the text literature, many topic models were proposed to represent documents and words as topics or latent topics in order to process text effectively and accurately. In this paper, we propose LDACLM or Latent Dirichlet Allocation Category LanguageModel for text categorization and estimate parameters of models by variational inference. As a variant of Latent Dirichlet Allocation Model, LDACLM regards documents of category as Language Model and uses variational parameters to estimate maximum a posteriori of terms. In general, experiments show LDACLM model is effective and outperform Na¨?ve Bayes with Laplace smoothing and Rocchio algorithm but little inferior to SVM for text categorization.

Copyright

© 2009, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Shibin Zhou
AU  - Kan Li
AU  - Yushu Liu
PY  - 2009
DA  - 2009/12/01
TI  - Text Categorization Based on Topic Model
JO  - International Journal of Computational Intelligence Systems
SP  - 398
EP  - 409
VL  - 2
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2009.2.4.8
DO  - 10.2991/ijcis.2009.2.4.8
ID  - Zhou2009
ER  -