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

School of Electronics, Electrical Engineering and Computer Science

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

8 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	United States
Publisher	Institute of Electrical and Electronics Engineers Inc.
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 - 2011

Bibliographical note

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

Access to Document

10.1109/WI-IAT.2011.73

Cite this

@inproceedings{95f1e3249c174ca3998cc1f25258440d,

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",

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

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 = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

}

TY - GEN

T1 - A Twofold-LDA Model for Customer Review Analysis

AU - Burns, Nicola

AU - Bi, Yaxin

AU - Wang, Hui

AU - Anderson, Terry

N1 - 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology ; Conference date: 01-01-2011

PY - 2011

Y1 - 2011

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.

U2 - 10.1109/WI-IAT.2011.73

DO - 10.1109/WI-IAT.2011.73

M3 - Conference contribution

SN - 978-1-4577-1373-6

VL - 1

SP - 253

EP - 256

BT - Unknown Host Publication

PB - Institute of Electrical and Electronics Engineers Inc.

CY - United States

ER -

A Twofold-LDA Model for Customer Review Analysis

Abstract

Bibliographical note

Access to Document

Fingerprint

Cite this