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.

Wineinformatics is a field that uses machine-learning and data-mining techniques to glean useful information from wine. In this work, attributes extracted from a large dataset of over 100,000 wine reviews are used to make predictions on two variables: quality based on a “100-point scale”, and price per 750 mL bottle. These predictions were built using support vector regression. Several evaluation metrics were used for model evaluation. In addition, these regression models were compared to classification accuracies achieved in a prior work. When regression was used for classification, the results were somewhat poor; however, this was expected since the main purpose of the regression was not to classify the wines. Therefore, this paper also compares the advantages and disadvantages of both classification and regression. Regression models can successfully predict within a few points of the correct grade of a wine. On average, the model was only 1.6 points away from the actual grade and off by about $13 per bottle of wine. To the best of our knowledge, this is the first work to use a large-scale dataset of wine reviews to perform regression predictions on grade and price.