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

YÜCEL A., Dag A., Oztekin A., Carpenter M.

Journal of Business Research, vol.151, pp.287-297, 2022 (SSCI, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 151
  • Publication Date: 2022
  • Doi Number: 10.1016/j.jbusres.2022.06.062
  • Journal Name: Journal of Business Research
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, International Bibliography of Social Sciences, Periodicals Index Online, ABI/INFORM, Business Source Elite, Business Source Premier, CAB Abstracts, INSPEC, Psycinfo, Public Affairs Index, Veterinary Science Database
  • Page Numbers: pp.287-297
  • Keywords: Sentiment analysis, Text mining, Business analytics, Data mining, Classification prediction
  • Ankara Yıldırım Beyazıt University Affiliated: Yes

Abstract

© 2022 Elsevier Inc.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.