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Open AccessArticle

Wineinformatics: A Quantitative Analysis of Wine Reviewers

Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA
Department of Computer Science, University of Alabama, Tuscaloosa, AL 35487, USA
Author to whom correspondence should be addressed.
Fermentation 2018, 4(4), 82;
Received: 31 July 2018 / Revised: 14 September 2018 / Accepted: 17 September 2018 / Published: 25 September 2018
(This article belongs to the Special Issue Bioprocess and Fermentation Monitoring)
Data Science is a successful study that incorporates varying techniques and theories from distinct fields including Mathematics, Computer Science, Economics, Business and domain knowledge. Among all components in data science, domain knowledge is the key to create high quality data products by data scientists. Wineinformatics is a new data science application that uses wine as the domain knowledge and incorporates data science and wine related datasets, including physicochemical laboratory data and wine reviews. This paper produces a brand-new dataset that contains more than 100,000 wine reviews made available by the Computational Wine Wheel. This dataset is then used to quantitatively evaluate the consistency of the Wine Spectator and all of its major reviewers through both white-box and black-box classification algorithms. Wine Spectator reviewers receive more than 87% accuracy when evaluated with the SVM method. This result supports Wine Spectator’s prestigious standing in the wine industry. View Full-Text
Keywords: wineinformatics; computational wine wheel; classification; wine reviewers ranking wineinformatics; computational wine wheel; classification; wine reviewers ranking
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Chen, B.; Velchev, V.; Palmer, J.; Atkison, T. Wineinformatics: A Quantitative Analysis of Wine Reviewers. Fermentation 2018, 4, 82.

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