@inproceedings{ca59e6f56c74435e85603fd83105dd02,
title = "A text classification method based on latent topics",
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.",
keywords = "Indexing by LDA, Latent topic, Text classification",
author = "Yanshan Wang and Choi, {In Chan}",
note = "Copyright: Copyright 2012 Elsevier B.V., All rights reserved.; 1st International Conference on Operations Research and Enterprise Systems, ICORES 2012 ; Conference date: 04-02-2012 Through 06-02-2012",
year = "2012",
language = "English (US)",
isbn = "9789898425973",
series = "ICORES 2012 - Proceedings of the 1st International Conference on Operations Research and Enterprise Systems",
pages = "212--214",
booktitle = "ICORES 2012 - Proceedings of the 1st International Conference on Operations Research and Enterprise Systems",
}