A TEXT CLASSIFICATION METHOD BASED ON LATENT TOPICS

Yanshan Wang, In-Chan Choi

2012

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.

References

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Paper Citation


in Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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