Classification and optimization of product review information using soft computing models

Tan, Choo Jun, Lim, Chee Peng, Cheah, Yu-N and Tan, Shing Chiang 2013, Classification and optimization of product review information using soft computing models, in ISAE 2013 : Proceedings of the Affective Engineering 2013 International Symposium, Japan Society of Kansei Engineering, Tokyo, Japan, pp. 115-120.

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Title Classification and optimization of product review information using soft computing models
Author(s) Tan, Choo Jun
Lim, Chee Peng
Cheah, Yu-N
Tan, Shing Chiang
Conference name Affective Engineering. International Symposium (1st : 2013 : Kitakyushu, Japan)
Conference location Kitakyushu, Japan
Conference dates 6 - 8 Mar. 2013
Title of proceedings ISAE 2013 : Proceedings of the Affective Engineering 2013 International Symposium
Editor(s) [Unknown]
Publication date 2013
Conference series Affective Engineering International Symposium
Start page 115
End page 120
Total pages 6
Publisher Japan Society of Kansei Engineering
Place of publication Tokyo, Japan
Keyword(s) product reviews
support vector machine
modified micro genetic algorithm
classification
multi-objective optimization
Summary A soft computing framework to classify and optimize text-based information extracted from customers' product reviews is proposed in this paper. The soft computing framework performs classification and optimization in two stages. Given a set of keywords extracted from unstructured text-based product reviews, a Support Vector Machine (SVM) is used to classify the reviews into two categories (positive and negative reviews) in the first stage. An ensemble of evolutionary algorithms is deployed to perform optimization in the second stage. Specifically, the Modified micro Genetic Algorithm (MmGA) optimizer is applied to maximize classification accuracy and minimize the number of keywords used in classification. Two Amazon product reviews databases are employed to evaluate the effectiveness of the SVM classifier and the ensemble of MmGA optimizers in classification and optimization of product related keywords. The results are analyzed and compared with those published in the literature. The outputs potentially serve as a list of impression words that contains useful information from the customers' viewpoints. These impression words can be further leveraged for product design and improvement activities in accordance with the Kansei engineering methodology.
ISSN 2187-669X
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30062607

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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