 A novel text analytic methodology for classification of product and service reviewsAhmet Yucel, Ali Dag, Asil Oztekin and Mark Carpenter Journal of Business Research, 2022, vol. 151, issue C, 287-297 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. Keywords: Sentiment analysis; Text mining; Business analytics; Data mining; Classification prediction (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:151:y:2022:i:c:p:287-297 DOI: 10.1016/j.jbusres.2022.06.062 Access Statistics for this article Journal of Business Research is currently edited by A. G. Woodside More articles in Journal of Business Research from Elsevier |
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