A Twofold-LDA Model for Customer Review Analysis

Nicola Burns, Yaxin Bi, Hui Wang, Terry Anderson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

The Latent Dirichlet Allocation model is an unsupervised generative model that is widely used for topic modelling in text. We propose to add supervision to the model in the form of domain knowledge to direct the focus of topics to more relevant aspects than the topics produced by standard LDA. Experimental results demonstrate the effectiveness of our method. We also propose a novel Twofold-LDA model to improve the current output of LDA in order to visualize results in graphical form, which can ultimately be used by potential customers. Experiments show the benefit of this new output, with the ability to produce topics focused on our desired aspects in a user friendly chart.
Original languageEnglish
Title of host publicationUnknown Host Publication
Place of PublicationWeb Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on (Volume:1 )
PublisherIEEE
Pages253-256
Number of pages4
Volume1
ISBN (Print)978-1-4577-1373-6
DOIs
Publication statusPublished - 2011
Event2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology -
Duration: 1 Jan 2011 → …

Conference

Conference2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
Period1/01/11 → …

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  • Cite this

    Burns, N., Bi, Y., Wang, H., & Anderson, T. (2011). A Twofold-LDA Model for Customer Review Analysis. In Unknown Host Publication (Vol. 1, pp. 253-256). IEEE. https://doi.org/10.1109/WI-IAT.2011.73