Table Of ContentSpringerBriefs in Computer Science
Bernard Chen
Wineinformatics
A New Data Science
Application
SpringerBriefs in Computer Science
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StanZdonik,BrownUniversity,Providence,RI,USA
ShashiShekhar,UniversityofMinnesota,Minneapolis,MN,USA
XindongWu,UniversityofVermont,Burlington,VT,USA
LakhmiC.Jain,UniversityofSouthAustralia,Adelaide,SA,Australia
DavidPadua,UniversityofIllinoisUrbana-Champaign,Urbana,IL,USA
XueminShermanShen,UniversityofWaterloo,Waterloo,ON,Canada
BorkoFurht,FloridaAtlanticUniversity,BocaRaton,FL,USA
V.S.Subrahmanian,UniversityofMaryland,CollegePark,MD,USA
MartialHebert,CarnegieMellonUniversity,Pittsburgh,PA,USA
KatsushiIkeuchi,UniversityofTokyo,Tokyo,Japan
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Bernard Chen
Wineinformatics
A New Data Science Application
BernardChen
UniversityofCentralArkansas
Conway,AR,USA
ISSN2191-5768 ISSN2191-5776 (electronic)
SpringerBriefsinComputerScience
ISBN978-981-19-7368-0 ISBN978-981-19-7369-7 (eBook)
https://doi.org/10.1007/978-981-19-7369-7
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Preface
Wineisanancientbeverageproducedandenjoyedbyhumansforseveralthousand
years, and it has become more popular and affordable nowadays. Hundred million
hectoliters of wine was produced across more than 30 countries each year. During
the grape growing, wine making, and wine evaluating process, lots of data are
generated and stored. With the right data mining/data science techniques, hidden
knowledge and information can be retrieved from the large amount of wine-
relateddata.
This book introduces a new data science application domain named
Wineinformatics which combines wine-related data with data mining/data science
techniquestodiscovernewtypesofknowledgeinwine.Supervisedlearning,which
isoneoffourlearningtypesofalgorithms,appliedonprofessionalwinereviewsis
the main focus used in this book. With the development and utilization of the
Computational Wine Wheel, large volumes of wine reviews in human language
format are converted into computer understandable binary encodings. New infor-
mationcanbeminedthroughaproperlydesigneddatasettoanswersometoughand
interestingquestionsonwine.
Chapter 1 provides an introduction to data science and Wineinformatics.
Chapter 2 introduces the development and usage of the Computational Wine
Wheel and the three datasets used in this book. Chapter 3 presents a set of experi-
ments to answer the question of “How does a wine achieve 90+ scores?” using
classificationalgorithms.Chapter4discusseshowtoevaluatewinejudgesandfigure
outthequestionof“Arewinereviewersreliableandconsistent?”Chapter5targets
the question of “Can actual wine grade and price be predicted through their
reviews?”usingregressionanalysis.Chapter6introducesnewerandmorecompli-
cated computer science techniques, Multi-Label and Multi-Target, to
Wineinformatics. These techniques are useful to work on the problem of “Can
wine grade, price and region be predicted altogether with higher accuracy?”
v
vi Preface
Chapter7discusseshowtoextractmoreinformationfromtheComputationalWine
Wheeltorespondtothequestionof“Howcancomputersunderstandwinereviews
even more?” Finally, Chapter 8 draws the conclusion and provides some of many
intriguingfutureworks.
Conway,AR,USA BernardChen
Contents
1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 DataScience. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Wineinformatics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 BookOverview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 DataCollectionandPreprocessing. . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 WineSensoryReviews. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 WineSpectator. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 ComputationalWineWheel. . .. . . . .. . . . .. . . . .. . . . .. . . . .. . 6
2.3.1 WineAromaWheel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3.2 BagofWordsinNaturalLanguageProcessing. . . . . . . . . . 8
2.3.3 DevelopmentoftheComputationalWineWheel(CWW). . . 8
2.3.4 HowtoUsetheCWW2.0. . . . . . . . . . . . . . . . . . . . . . . . . 10
2.4 Dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4.1 Dataset1:TheBigDataset. . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4.2 Dataset2:Twenty-FirstCenturyBordeauxDataset. . . . . . . 15
2.4.3 Dataset3:Twenty-FirstCenturyEliteBordeaux. . . . . . . . . 15
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 ClassificationinWineinformatics. . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1 ClassificationandClassificationLabel. . . . . . . . . . . . . . . . . . . . . . 17
3.2 ClassificationAlgorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2.1 SupportVectorMachines(SVM). . . . . . . . . . . . . . . . . . . . 18
3.2.2 NaïveBayesClassifier. . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3 ClassificationEvaluations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.1 FivefoldCrossValidation. . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.3.2 EvaluationMetrics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
vii
viii Contents
3.4 HowDoesaWineAchieve90+Scores?. . . . . . . . . . . . . . . . . . . . 24
3.4.1 Dataset1:TheBigDatasetResults. . . . . . . . . . . . . . . . . . . 24
3.4.2 Dataset2:Twenty-FirstCenturyBordeauxDataset. . . . . . . 25
3.4.3 Dataset3:Twenty-FirstCenturyEliteBordeaux. . . . . . . . . 25
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4 EvaluationofWineJudges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.1 WineReviewersinWineSpectator. . . . . . . . . . . . . . . . . . . . . . . . 29
4.2 HowtoEvaluateWineReviewers. . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3 RankingoftheWineReviewersUsingNaïveBayesClassifier. . . . . 31
4.4 RankingoftheWineReviewersUsingSVM. . . . . . . . . . . . . . . . . 34
4.5 ComparisonofNaïveBayesandSVM. . . . . . . . . . . . . . . . . . . . . . 36
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
5 RegressioninWineinformatics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5.1 RegressionAnalysisData. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5.2 Methods. . . . . .. . . . . .. . . . . .. . . . .. . . . . .. . . . . .. . . . .. . . . 40
5.2.1 SupportVectorRegression(SVR). . . . . . . . . .. . . . . . . . . . 40
5.2.2 EvaluationMetrics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.3 RegressionAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.3.1 CanWineGradeBePredictedThroughTheirReviews?. . . . 42
5.3.2 CanWinePriceBePredictedThroughTheirReviews?. . .. 43
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
6 Multi-Class,Multi-LabelandMulti-TargetinWineinformatics. . . .. 45
6.1 Multi-Class,Multi-LabelandMulti-Target. . . . . . . . . . . . . . . . . . . 45
6.2 WineLabelsforMulti-TargetPrediction. . . . . . . . . . . . . . . . . . . . 46
6.3 HowtoPredictMulti-LabelandMulti-TargetProblems?. . . . . . . . . 47
6.3.1 BinaryRelevance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
6.3.2 LabelPowerset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
6.3.3 ClassifierChains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
6.3.4 BayesianClassifierChainsandConditionalDependency
Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
6.4 EvaluationMetrics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
6.5 ImplementationofMulti-LabelandMulti-Target
inWineinformatics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
6.5.1 ApplyMulti-LabelontheBigDataset. . . . . . . . . . . . . . . . . 51
6.5.2 ApplyMulti-TargetontheBigDataset. . . . . . . . . . . . . . . . 53
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
7 AdvancedUsageoftheComputationalWineWheel. . . . . . . . . . . . . . 57
7.1 AdditionalInformationCapturedbytheComputationalWine
Wheel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
7.2 NaïveBayesClassificationAlgorithmforMixedDatatype. . . . . . . 60
Contents ix
7.3 DoesAdditionalAttributesHelp?. . . . . . . . . . . . . . . . . . . . . . . . . 61
7.3.1 CaseStudyontheTwenty-FirstCenturyBordeaux
Dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
7.3.2 CaseStudyontheTwenty-FirstCenturyEliteBordeaux
Dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
7.3.3 TheMoreAttributes,theBetter?. . . . . . . . . . . . . . . . . . . . . 63
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
8 ConclusionandFutureWorks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
8.1 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
8.2 FutureWorks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
