Predicting Future Importance of Product Features Based on Online Customer Reviews

Huimin Jiang, Chun Kit Kwong, Kai Leung Yung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)

Abstract

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.
Original languageEnglish
Article number111413
JournalJournal of Mechanical Design, Transactions Of the ASME
Volume139
Issue number11
DOIs
Publication statusPublished - 1 Nov 2017

Keywords

  • Fuzzy inference method
  • Fuzzy time series method
  • New product development
  • Opinion mining
  • Product features

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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