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Gestão & Produção

versión impresa ISSN 0104-530Xversión On-line ISSN 1806-9649


SANTOS, Andrey Schmidt dos; CAMARGO, Luis Felipe Riehs  y  LACERDA, Daniel Pacheco. Evaluation of classification techniques for identifying fake reviews about products and services on the internet. Gest. Prod. [online]. 2020, vol.27, n.4, e4672.  Epub 27-Jul-2020. ISSN 1806-9649.

With the e-commerce growth, more people are buying products over the internet. To increase customer satisfaction, merchants provide spaces for product and service reviews. Products with positive reviews attract customers, while products with negative reviews lose customers. Following this idea, some individuals and corporations write fake reviews to promote their products and services or defame their competitors. The difficulty for finding these reviews was in the large amount of information available. One solution is to use data mining techniques and tools, such as the classification function. Exploring this situation, the present work evaluates classification techniques to identify fake reviews about products and services on the Internet. The research also presents a literature systematic review on fake reviews. The research used 8 classification algorithms. The algorithms were trained and tested with a hotels database. The CONCENSO algorithm presented the best result, with 88% in the precision indicator. After the first test, the algorithms classified reviews on another hotels database. To compare the results of this new classification, the Review Skeptic algorithm was used. The SVM and GLMNET algorithms presented the highest convergence with the Review Skeptic algorithm, classifying 83% of reviews with the same result. The research contributes by demonstrating the algorithms ability to understand consumers’ real reviews to products and services on the Internet. Another contribution is to be the pioneer in the investigation of fake reviews in Brazil and in production engineering.

Palabras clave : Fake reviews; Text classification; Knowledge discovery in databases; Text mining.

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