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Automated Extraction of Typical Expressions Describing Product Features from Customer Reviews

Author

Listed:
  • Karel Barák

    (Mendel University in Brno, Czech Republic)

  • František Dařena

    (Mendel University in Brno, Czech Republic)

  • Jan Žižka

    (Mendel University in Brno, Czech Republic)

Abstract

The paper presents a procedure that helps in revealing topics hidden in large collections of textual documents (such as customer reviews) related to a certain group of products or services. Together with identification of the groups containing the topics the lists of important expressions is presented which helps in understanding what characterizes these aspects most typically from the semantic point of view. The procedure includes determining an appropriate number of groups representing the prevailing topics, partitioning the documents into a desired number of groups using clustering, extracting significant typical features of documents from each group with application of feature selection methods, and evaluating the outcomes with the assistance of a human expert. The results show that the presented approach, consisting mostly of automated steps, is able to separate and characterize the aspects of a certain product as discussed by the customers and be later useful, e.g., for handling customer complaints, designing promotional campaigns, or improving the products.

Suggested Citation

  • Karel Barák & František Dařena & Jan Žižka, 2015. "Automated Extraction of Typical Expressions Describing Product Features from Customer Reviews," European Journal of Business Science and Technology, Mendel University in Brno, Faculty of Business and Economics, vol. 1(2), pages 83-92.
    • Handle: RePEc:men:journl:v:1:y:2015:i:2:p:83-92
    DOI: 10.11118/ejobsat.v1i2.27
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    References listed on IDEAS

    as
    1. MacKenzie, Scott B. & Podsakoff, Philip M., 2012. "Common Method Bias in Marketing: Causes, Mechanisms, and Procedural Remedies," Journal of Retailing, Elsevier, vol. 88(4), pages 542-555.
    2. Alpu, Ozlem, 2015. "A methodology for evaluating satisfaction with high-speed train services: A case study in Turkey," Transport Policy, Elsevier, vol. 44(C), pages 151-157.
    3. Engler, Tobias H. & Winter, Patrick & Schulz, Michael, 2015. "Understanding online product ratings: A customer satisfaction model," Journal of Retailing and Consumer Services, Elsevier, vol. 27(C), pages 113-120.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    product aspects identification; text mining; cluster analysis; feature selection;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other

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