Wineinformatics: Comparing and Combining SVM Models Built by Wine Reviews from Robert Parker and Wine Spectator for 95 + Point Wine Prediction

Abstract

Wineinformatics is among the new fields in data science that use wine as domain knowledge. To process large amounts of wine review data in human language format, the computational wine wheel is applied. In previous research, the computational wine wheel was created and applied to different datasets of wine reviews developed by Wine Spectator. The goal of this research is to explore the development and application of the computational wine wheel to reviews from a different reviewer, Robert Parker. For comparison, this research collects 513 elite Bordeaux wines that were reviewed by both Robert Parker and Wine Spectator. The full power of the computational wine wheel is utilized, including NORMALIZED, CATEGORY, and SUBCATEGORY attributes. The datasets are then used to predict whether the wine is a classic wine (95 + scores) or not (94 − scores) using the black-box classification algorithm support vector machine. The Wine Spectator’s dataset, with a combination of NORMALIZED, CATEGORY, and SUBCATEGORY attributes, achieves the best accuracy of 76.02%. Robert Parker’s dataset also achieves an accuracy of 75.63% out of all the attribute combinations, which demonstrates the usefulness of the computational wine wheel and that it can be effectively adopted in different wine reviewers’ systems. This paper also attempts to build a classification model using both Robert Parker’s and Wine Spectator’s reviews, resulting in comparable prediction power.