Research Papers: Variability/Uncertainty in D3

Predicting Future Importance of Product Features Based on Online Customer Reviews

[+] Author and Article Information
Huimin Jiang

Department of Industrial and
Systems Engineering,
The Hong Kong Polytechnic University,
Hong Kong, China
e-mail: huimin.jiang@connect.polyu.hk

C. K. Kwong

Department of Industrial and
Systems Engineering,
The Hong Kong Polytechnic University,
Hong Kong, China
e-mail: c.k.kwong@polyu.edu.hk

K. L. Yung

Department of Industrial and
Systems Engineering,
The Hong Kong Polytechnic University,
Hong Kong, China
e-mail: kl.yung@polyu.edu.hk

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 24, 2017; final manuscript received July 6, 2017; published online October 2, 2017. Assoc. Editor: Yan Wang.

J. Mech. Des 139(11), 111413 (Oct 02, 2017) (10 pages) Paper No: MD-17-1173; doi: 10.1115/1.4037348 History: Received February 24, 2017; Revised July 06, 2017

Previous studies conducted customer surveys based on questionnaires and interviews, and the survey data were then utilized to analyze product features. In recent years, online customer reviews on products became extremely popular, which contain rich information on customer opinions and expectations. However, previous studies failed to properly address the determination of the importance of product features and prediction of their future importance based on online reviews. Accordingly, a methodology for predicting future importance weights of product features based on online customer reviews is proposed in this paper which mainly involves opinion mining, a fuzzy inference method, and a fuzzy time series method. Opinion mining is adopted to analyze the online reviews and extract product features. A fuzzy inference method is used to determine the importance weights of product features using both frequencies and sentiment scores obtained from opinion mining. A fuzzy time series method is adopted to predict the future importance of product features. A case study on electric irons was conducted to illustrate the proposed methodology. To evaluate the effectiveness of the fuzzy time series method in predicting the future importance, the results obtained by the fuzzy time series method are compared with those obtained by the three common forecasting methods. The results of the comparison show that the prediction results based on fuzzy time series method are better than those based on exponential smoothing, simple moving average, and fuzzy moving average methods.

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Grahic Jump Location
Fig. 1

Membership functions of frequencies and sentiment scores of product features

Grahic Jump Location
Fig. 2

Membership functions of importance weights of product features

Grahic Jump Location
Fig. 3

Fuzzy inference process of determining importance weight of “weight”



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