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.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Predicting Future Importance of Product Features Based on Online Customer Reviews



    Published in:

    Publication date :

    2017




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    50.15 Konstruktionslehre / 52.15 Maschinenelemente, Maschinenbaugruppen / 52.20 Antriebstechnik, Getriebelehre
    Local classification TIB:    770/5315/5330



    Public Transit Customer Satisfaction Dimensions Discovery from Online Reviews

    Gao, Lu / Yu, Yao / Liang, Wuling | Springer Verlag | 2016

    Free access



    Analyzing Customer Preference to Product Optional Features in Supporting Product Configuration

    Sha, Zhenghui / Saeger, Veronica / Wang, Mingxian et al. | British Library Conference Proceedings | 2017


    Analyzing Customer Preference to Product Optional Features in Supporting Product Configuration

    Wang, Mingxian / Saeger, Veronica / Chen, Wei et al. | SAE Technical Papers | 2017