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ADM-LDA : an aspect detection model based on topic modelling using the structure of review sentences

Bagheri, A; Saraee, MH; de Jong, F

Authors

A Bagheri

F de Jong



Abstract

Probabilistic topic models are statistical methods whose aim is to discover the latent structure in a large collection of documents. The intuition behind topic models is that, by generating documents by latent topics, the word distribution for each topic can be modelled and the prior distribution over the topic learned. In this paper we propose to apply this concept by modelling the topics of sentences for the aspect detection problem in review documents in order to improve sentiment analysis systems. Aspect detection in sentiment analysis helps customers effectively navigate into detailed information about their features of interest. The proposed approach assumes that the aspects of words in a sentence form a Markov chain. The novelty of the model is the extraction of multiword aspects from text data while relaxing the bag-of-words assumption. Experimental results show that the model is indeed able to perform the task significantly better when compared with standard topic models.

Citation

Bagheri, A., Saraee, M., & de Jong, F. (2014). ADM-LDA : an aspect detection model based on topic modelling using the structure of review sentences. Journal of Information Science, 40(5), 621-636. https://doi.org/10.1177/0165551514538744

Journal Article Type Article
Online Publication Date Jun 11, 2014
Publication Date Oct 1, 2014
Deposit Date Jan 30, 2015
Journal Journal of Information Science
Print ISSN 0165-5515
Electronic ISSN 1741-6485
Publisher SAGE Publications
Peer Reviewed Peer Reviewed
Volume 40
Issue 5
Pages 621-636
DOI https://doi.org/10.1177/0165551514538744
Publisher URL http://dx.doi.org/10.1177/0165551514538744
Related Public URLs http://www.uk.sagepub.com/journals/Journal201676