An Improved LDA Model for Academic Document Analysis

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.