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.

Applying Neural Networks in Wineinformatics with the New Computational Wine Wheel

“…In contrast to the shorter and more concise reviews offered by Wine Spectator, Parker's reviews are more elaborating, colorful, and descriptive. As the result, reviews from Robert Parker pose greater challenges for natural language processing techniques [11]. In this research, the Computational Wine Wheel proved its adaptability on the Robert Parker dataset.…”

“…In [1], the work of all reviewers in Wine Spectator was studied and evaluated, which is considered as an intra-review site evaluation. In this research, like [11], inter-review site assessment is investigated. Based the criteria of similar a history of publication records for fair comparison and less contradicting reviews for the study of merging reviews purposes, Robert Parker's Wine Advocate was chosen as the second review site for data sources.…”

“…In the latest research [11], the CWW2.0 was applied to Robert Parker's reviews and then compared with reviews from Wine Spectator through computational model predicting whether a wine would receive 95+ scores on a carefully selected elite Bordeaux, France, data set, which is based on the famous list of the Bordeaux Wine Official Classification of 1855. The elite Bordeaux data set was gathered from Wine.com, comprising all 21st century (2000-2020) wines listed in the 1855 Bordeaux Wine Official Classification that had reviews from both Robert Parker and Wine Spectator.…”

“…Most of the focus of existing wineinformatics classification problems [1,2,9,11,27] are to predict whether a wine can score 90 points or higher: if a wine receives a score equal to or above 90 out of 100, it will be labeled as a positive (+) class. Conversely, if it scores below 90, it will be labeled as a negative (−) class.…”

“…The first two versions of the Computational Wine Wheel were built from Wine Spectator's reviews and applied on the data sets collected and developed from Wine Spectator's reviews for various clustering, classification, and regression model creation processes [1,2,[7][8][9]11]. Not until early 2022 was the Computational Wine Wheel applied to the data sets collected and developed from Robert Parker's reviews and compared directly with the data sets collected and developed from Wine Spectator reviews [9].…”