In this paper, we propose a new probabilistic generative model for topic analysis of online reviews, called Author-Experience-Object-Topic Model (AEOT). This model is to capture the relationship between the authors, objects and reviews in order to improve the performance of topic analysis. The model, as a general one, can be transformed to six simpler models, and can produce topic-word, author-topic and object-topic distributions. Experimental results show that the model is suitable for topic analysis of online reviews, and outperforms other existing methods. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Zhang, Y., Ji, D. H., Su, Y., & Hu, P. (2011). Topic analysis for online reviews with an author-experience-object-topic model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7097 LNCS, pp. 303–314). https://doi.org/10.1007/978-3-642-25631-8_28
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