Volume 9 Number 10 (Oct. 2014)
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JSW 2014 Vol.9(10): 2749-2757 ISSN: 1796-217X
doi: 10.4304/jsw.9.10.2749-2757

An Improved LDA Model for Academic Document Analysis

Yuyan Jiang, Yuan Shao

School of Management Science and Engineering/Anhui University of Technology, Ma’anshan, China

Abstract—Electronic documents on the Internet are always generated with many kinds of side information. Although those massive kinds of information make the analysis become very difficult, models would fit and analyze data well if they could make full use of those kinds of side information. This paper, base on the study on probabilistic topic model, proposes a new improved LDA model which is suitable for analysis of academic document. Based on the modification of standard LDA model, this new improved LDA model could analyze documents with both authors and references. To evaluate the generalization capability, this paper compares the new model with standard LDA and DMR model using the widely used Rexa dataset. Experimental results show that the new model has a high capability of document clustering and topics extraction than standard LDA and its modifications. In addition, the new model outperforms DMR model in task of authors discriminant.

Index Terms—academic documents; topic model; topics extraction; authors discriminant

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Cite: Yuyan Jiang, Yuan Shao, "An Improved LDA Model for Academic Document Analysis," Journal of Software vol. 9, no. 10, pp. 2749-2757, 2014.

General Information

ISSN: 1796-217X (Online)
Frequency:  Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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