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Authors: Yanshan Wang and In-Chan Choi

Affiliation: Korea University, Korea, Republic of

ISBN: 978-989-8425-97-3

Keyword(s): Text Classification, Latent Topic, Indexing by LDA.

Related Ontology Subjects/Areas/Topics: Data Mining and Business Analytics ; Information Systems ; Management Sciences ; Methodologies and Technologies ; Operational Research

Abstract: Latent Dirichlet Allocation (LDA) is a generative model, which exhibits superiority over other topic modelling algorithms on latent topics of text data. Indexing by LDA is a new method in the context of LDA to provide a new definition of document probability vectors that can be applied as feature vectors. In this paper, we propose a joint process of text classification that combines DBSCAN, indexing with LDA and Support Vector Machine (SVM). DBSCAN algorithm is applied as a pre-processing for LDA to determine the number of topics, and then LDA document indexing features are employed for text classifier SVM.

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Paper citation in several formats:
Wang, Y. and Choi, I. (2012). A TEXT CLASSIFICATION METHOD BASED ON LATENT TOPICS.In Proceedings of the 1st International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-8425-97-3, pages 212-214. DOI: 10.5220/0003740902120214

@conference{icores12,
author={Yanshan Wang. and In{-}Chan Choi.},
title={A TEXT CLASSIFICATION METHOD BASED ON LATENT TOPICS},
booktitle={Proceedings of the 1st International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2012},
pages={212-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003740902120214},
isbn={978-989-8425-97-3},
}

TY - CONF

JO - Proceedings of the 1st International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - A TEXT CLASSIFICATION METHOD BASED ON LATENT TOPICS
SN - 978-989-8425-97-3
AU - Wang, Y.
AU - Choi, I.
PY - 2012
SP - 212
EP - 214
DO - 10.5220/0003740902120214

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