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Performance evaluation of Latent Dirichlet Allocation in text mining

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4 Author(s)
Zelong Liu ; Sch. of Eng. & Design, Brunel Univ., Uxbridge, UK ; Maozhen Li ; Yang Liu ; Mahesh Ponraj

This paper introduces three classic models of statistical topic models: Latent Semantic Indexing (LSI), Probabilistic Latent Semantic Indexing (PLSI) and Latent Dirichlet Allocation (LDA). Then a method of text classification based on LDA model is briefly described, which uses LDA model as a text representation method. Each document means a probability distribution of fixed latent topic sets. Next, Support Vector Machine (SVM) is chose as classification algorithm. Finally, the evaluation parameters in classification system of LDA with SVM are higher than other two methods which are LSI with SVM and VSM with SVM, showing a better classification performance.

Published in:

Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on  (Volume:4 )

Date of Conference:

26-28 July 2011