Wineinformatics: Using the Full Power of the Computational Wine Wheel to Understand 21st Century Bordeaux Wines from the Reviews

Abstract

Although wine has been produced for several thousands of years, the ancient beverage has remained popular and even more affordable in modern times. Among all wine making regions, Bordeaux, France is probably one of the most prestigious wine areas in history. Since hundreds of wines are produced from Bordeaux each year, humans are not likely to be able to examine all wines across multiple vintages to define the characteristics of outstanding 21st century Bordeaux wines. Wineinformatics is a newly proposed data science research with an application domain in wine to process a large amount of wine data through the computer. The goal of this paper is to build a high-quality computational model on wine reviews processed by the full power of the Computational Wine Wheel to understand 21st century Bordeaux wines. On top of 985 binary-attributes generated from the Computational Wine Wheel in our previous research, we try to add additional attributes by utilizing a CATEGORY and SUBCATEGORY for an additional 14 and 34 continuous-attributes to be included in the All Bordeaux (14,349 wine) and the 1855 Bordeaux datasets (1359 wines). We believe successfully merging the original binary-attributes and the new continuous-attributes can provide more insights for Naïve Bayes and Supported Vector Machine (SVM) to build the model for a wine grade category prediction. The experimental results suggest that, for the All Bordeaux dataset, with the additional 14 attributes retrieved from CATEGORY, the Naïve Bayes classification algorithm was able to outperform the existing research results by increasing accuracy by 2.15%, precision by 8.72%, and the F-score by 1.48%. For the 1855 Bordeaux dataset, with the additional attributes retrieved from the CATEGORY and SUBCATEGORY, the SVM classification algorithm was able to outperform the existing research results by increasing accuracy by 5%, precision by 2.85%, recall by 5.56%, and the F-score by 4.07%. The improvements demonstrated in the research show that attributes retrieved from the CATEGORY and SUBCATEGORY has the power to provide more information to classifiers for superior model generation. The model build in this research can better distinguish outstanding and class 21st century Bordeaux wines. This paper provides new directions in Wineinformatics for technical research in data science, such as regression, multi-target, classification and domain specific research, including wine region terroir analysis, wine quality prediction, and weather impact examination.

The importance of outlier rejection and significant explanatory variable selection for pinot noir wine soft sensor development

“… There are no links between the input data and attributes. Typically, input data or attributes influence the prediction of output data with equal weight ( Dong et al, 2021 ) K-nearest neighbour (KNN) is a method for classifying objects based on the training examples in the feature space that is closest to the target object. See Table 1 Application Wine quality ( Bhardwaj et al, 2022 ) Wine quality ( Dong et al, 2020 ) Wine quality ( Bhardwaj et al, 2022 ) 1.…”

Could Collected Chemical Parameters Be Utilized to Build Soft Sensors Capable of Predicting the Provenance, Vintages, and Price Points of New Zealand Pinot Noir Wines Simultaneously?

“…For Pinot noir wines' product extrinsic cues, please check Table S2 Wineinformatics incorporates data science and wine-related datasets, including physicochemical laboratory data and wine reviews, to discover useful information for wine producers, distributors, and consumers [30]. Typically, Naive Bayes, k-nearest neighbour (KNN), decision tree, support vector machine (SVM) and Random Forrest are used in wineinformatics (Table 2) [12,[30][31][32][33][34][35][36].…”

“…There is no relationship between the input data and attributes. Typically, input data or attributes influence the prediction of output data with equal weight [32] K-nearest neighbour (KNN) is a simple, straightforward machine learning algorithm that can be used to solve classification and regression problems [30].…”

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

“…The wine score was converted to the class label based on classification problems. Most previous Wineinformatics studies [10,16,24] targeted the classification problem regarding the prediction of whether a wine can receive 90 points or above; thus, if the wine received a score equal to or above 90 points out of 100, the label of the wine was marked as a positive (+) class. Otherwise, the label was marked as a negative (−) class.…”

“…The computational wine wheel was developed based on more than 1000 wine reviews from Wine Spectator [11]. After preprocessing using the computational wine wheel, datasets of Wine Spectator's reviews were used in a variety of different topics [12,13], for example, to make predictions for three different targets, namely, price per 750mL bottles, quality based on a 100-point scale, and style derived from the region of origin [14]; to test if wine reviews can be used to predict whether a bottle of wine can be held for six years or more before it reaches the optimal conditions for drinking [15]; and to evaluate Wine Spectator and all of its major reviews using both white-box and black-box classification algorithms [16]. In this research, the computational wine wheel is applied to a carefully developed elite Bordeaux dataset, which contains more than 500 wines, with reviews from both Wine Spectator and Robert Parker.…”