A Twofold-LDA Model for Customer Review Analysis

Nicola Burns; Yaxin Bi; Hui Wang; Terry Anderson

doi:10.1109/WI-IAT.2011.73

A Twofold-LDA Model for Customer Review Analysis

Nicola Burns, Yaxin Bi, Hui Wang, Terry Anderson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 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 language	English
Title of host publication	Unknown Host Publication
Place of Publication	Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on (Volume:1 )
Publisher	IEEE
Pages	253-256
Number of pages	4
Volume	1
ISBN (Print)	978-1-4577-1373-6
DOIs	https://doi.org/10.1109/WI-IAT.2011.73
Publication status	Published (in print/issue) - 2011
Event	2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Duration: 1 Jan 2011 → …

Conference

Conference	2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
Period	1/01/11 → …

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10.1109/WI-IAT.2011.73

Cite this

@inproceedings{3dd8917dc3954eb89ad9d64a32aca18e,

title = "A Twofold-LDA Model for Customer Review Analysis",

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.",

author = "Nicola Burns and Yaxin Bi and Hui Wang and Terry Anderson",

year = "2011",

doi = "10.1109/WI-IAT.2011.73",

language = "English",

isbn = "978-1-4577-1373-6",

volume = "1",

pages = "253--256",

booktitle = "Unknown Host Publication",

publisher = "IEEE",

address = "United States",

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

}

Burns, N, Bi, Y, Wang, H & Anderson, T 2011, A Twofold-LDA Model for Customer Review Analysis. in Unknown Host Publication. vol. 1, IEEE, Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on (Volume:1 ), pp. 253-256, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 1/01/11. https://doi.org/10.1109/WI-IAT.2011.73

A Twofold-LDA Model for Customer Review Analysis. / Burns, Nicola; Bi, Yaxin; Wang, Hui et al.
Unknown Host Publication. Vol. 1 Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on (Volume:1 ): IEEE, 2011. p. 253-256.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Bi, Yaxin

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AU - Anderson, Terry

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N2 - 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.

AB - 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.

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DO - 10.1109/WI-IAT.2011.73

M3 - Conference contribution

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BT - Unknown Host Publication

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ER -

A Twofold-LDA Model for Customer Review Analysis

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