Wineinformatics: A Quantitative Analysis of Wine Reviewers

Chen, Bernard; Velchev, Valentin; Palmer, James N.; Atkison, Travis

doi:10.3390/fermentation4040082

Cited by 19 publications

(23 citation statements)

References 20 publications

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“…In either case, this research suggests that attributes retrieved from the CATEGORY and SUBCATEGORY have the power to provide more information to classifiers for superior model generation. This finding provides strong impact to many Wineinformatics research efforts in different wine-related topics, such as evaluation on wine reviewers [11], wine grade and price regression analysis [12], terroir study in a single wine region [48], weather impacts examination [49], and multi-target classification on a wine's score, price, and region [50].…”

Section: Comparison Of Datasets 4 Andmentioning

confidence: 91%

“…The magazine publishes 15 issues a year, and there are between 400 and 1000 wine reviews per issue. In our previous Wineinformatics research [11,12], more than 100,000 wine reviews were gathered and analyzed across all wine regions. This dataset was used to test wine reviewers' accuracy in predicting a wine's credit score.…”

Section: Wine Spectatormentioning

confidence: 99%

“…This dataset was used to test wine reviewers' accuracy in predicting a wine's credit score. Wine Spectator reviewers received more than 87% accuracy when evaluated with the SVM method while predicting whether a wine received a credit score higher than 90/100 points [11]. The regression analysis on predicting a wine's actual score based on the reviews also has an outstanding result with only a 1.6 average score difference on a Mean Absolute Error (MAE) evaluation [12].…”

Section: Wine Spectatormentioning

confidence: 99%

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

2021

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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.

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Section: Comparison Of Datasets 4 Andmentioning

confidence: 91%

Section: Wine Spectatormentioning

confidence: 99%

Section: Wine Spectatormentioning

confidence: 99%

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

2021

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“…This paved the way for performing analyses like visualization, summarization, content classification and clustering, and semantic and sentiment analysis on chunks of unstructured text data (Miner et al, 2012). Such applications in the domain of wine review (Chen et al, 2014; Chen et al, 2018; McCannon, 2020) have been used to efficiently extract features in wine reviews that can be incorporated in further analysis.…”

Section: Introductionmentioning

confidence: 99%

Using Neural Network Models for Wine Review Classification

2022

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Wines are usually evaluated by wine experts and enthusiasts who give numeric ratings as well as text reviews. While most wine classification studies have been based on conventional statistical models using numeric variables, there has been very limited work on implementing neural network models using wine reviews. In this paper, we apply neural network models (CNN, BiLSTM, and BERT) to extract useful information from wine reviews and classify wines according to different rating classes. Using a large collection of wine reviews from Wine Spectator, the study shows that BERT, a neural network framework recently developed by Google, has the best performance. In the two-class classification (90–100 and 80–89), BERT achieves an accuracy of 89.12%, followed by BiLSTM (88.69%) and CNN (88.02%). In the four-class classification (95–100, 90–94, 85–89, and 80–84), BERT yields an 81.57% accuracy, while the other two produce an 80% accuracy. The neural network models in the paper are independent of domain knowledge and thus can be easily extended to other kinds of text analysis. Expanding the limited work on wine text review classification studies, these models are up-to-date and provide valuable additions to wine data analysis. (JEL Classifications: C45, C88, D83)

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“…An endless number of varieties and flavors are provided to consumers; as not many of whom are wine experts, their choices in wines can be influenced by the reviews and scores that reputed experts and websites assign them. Therefore, what they have to say about the quality of produced wines can be relied upon when manufacturing them [5]. Beneficiaries of these wine reviews do not consist of only consumers; winemakers can also gain valuable information and knowledge from expert reviews in knowing which factors contribute most to whether a wine should be drunk or held.…”

Section: Introductionmentioning

confidence: 99%

Wineinformatics: Can Wine Reviews in Bordeaux Reveal Wine Aging Capability?

et al. 2021

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Wineinformatics is a new and emerging data science that uses wine as domain knowledge and integrates data systems and wine-related data sets. Wine reviews from Wine Spectator usually include the aging information, at the end of the review, in the form of “Best from YearA through YearB”; with the vintage of the wine included, the suggested holding year (YearA—vintage), shelf-life (YearB—vintage) and aging capacity (YearB—YearA) can be calculated and provide crucial information in the study of wineinformatics. The goal of this paper is to test whether wine reviews describing olfactory and gustatory information reveal wines’ suggested holding-year information. Wine reviews from Wine Spectator are extracted and processed by a natural language processing tool named the Computational Wine Wheel for categorizing and mapping various wine terminologies from wine reviews into a consolidated set of descriptors. The suggested aging capability is also calculated from the review and served as a label for classification problems. The study uses different learning algorithms, analyzing their performances and using the best-performing algorithm(s) to build a model for the prediction of a wine’s aging properties. The results of the study suggest that both support vector machine (SVM) and the K-nearest neighbor (KNN) algorithms achieved more than 70% accuracy. These results suggest that the algorithms are able of capturing a hidden informational relationship between a wine’s reviews and its aging capability.

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Wineinformatics: A Quantitative Analysis of Wine Reviewers

Cited by 19 publications

References 20 publications

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

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

Using Neural Network Models for Wine Review Classification

Wineinformatics: Can Wine Reviews in Bordeaux Reveal Wine Aging Capability?

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