Volume 2, Issue 4, December 2009, Pages 398 - 409
Text Categorization Based on Topic Model
Shibin Zhou, Kan Li, Yushu Liu
Received 29 December 2008, Accepted 19 August 2009, Available Online 1 December 2009.
- https://doi.org/10.2991/ijcis.2009.2.4.8How to use a DOI?
- Topic model, Latent Dirichlet allocation, Variational Inference, Category LanguageModel.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Shibin Zhou AU - Kan Li AU - Yushu Liu PY - 2009 DA - 2009/12 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 - https://doi.org/10.2991/ijcis.2009.2.4.8 ID - Zhou2009 ER -