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

Bagheri, A, Saraee, MH and 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) , pp. 621-636.

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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.

Item Type: Article
Themes: Memory, Text and Place
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: Journal of Information Science
Publisher: SAGE Publications
Refereed: Yes
ISSN: 0165-5515
Related URLs:
Funders: Non funded research
Depositing User: Dr Mo Saraee
Date Deposited: 30 Jan 2015 12:49
Last Modified: 29 Oct 2015 00:10

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