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

7 Citations (Scopus)


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 PublicationUnited States
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Print)978-1-4577-1373-6
Publication statusPublished - 2011

Bibliographical note

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology ; Conference date: 01-01-2011


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