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A novel text analytic methodology for classification of product and service reviews

Author

Listed:
  • Yucel, Ahmet
  • Dag, Ali
  • Oztekin, Asil
  • Carpenter, Mark

Abstract

Classifying the sentiments of online reviews of products or services is important in that it provides the analysts with the ability to extract critical information which can be used to improve the corresponding product or service. The objective of this study is to classify the customer reviews (on a five-star and binary scale) that were collected for four different types of products/services. To achieve this goal, a novel classification framework is built by devising a unique classifier (composite variable), which includes rich information gathered by using all of the extracted features. The proposed framework is compared to commonly used Singular Value Decomposition (SVD) and chi-square-based feature selection (selected features, SF). These approaches are separately deployed in tree-based machine learning algorithms and Logistic Regression using a five-fold cross validation strategy. The results indicate that the proposed methodology outperforms the alternatives for each dataset employed.

Suggested Citation

  • Yucel, Ahmet & Dag, Ali & Oztekin, Asil & Carpenter, Mark, 2022. "A novel text analytic methodology for classification of product and service reviews," Journal of Business Research, Elsevier, vol. 151(C), pages 287-297.
    • Handle: RePEc:eee:jbrese:v:151:y:2022:i:c:p:287-297
    DOI: 10.1016/j.jbusres.2022.06.062
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    References listed on IDEAS

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