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Wineinformatics

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-981-19-7369-7

ISBN: 978-981-19-7368-0

Publisher: Springer Nature Singapore - Springer Journals

Nov 30, 2022

Available

Introduction

Chen, Bernard

Pages: 1 –4

[This chapter provides an introduction to data science and Wineinformatics.]
Available

Data Collection and Preprocessing

Chen, Bernard

Pages: 5 –16

[Since wine reviews are written in human language format, computers cannot understand the meanings without proper preprocessing works. Therefore, “How to make computers understand wine reviews?” is the main problem to be solved in this chapter. How to develop and use the Computational Wine Wheel...
Available

Classification in Wineinformatics

Chen, Bernard

Pages: 17 –27

[“How does a wine achieve 90+ scores?” is the main problem to be solved in this chapter. In order to answer the question, the problem of classification with a target label, different types of classification algorithms and evaluation methods are presented. Classification models are built on all...
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Evaluation of Wine Judges

Chen, Bernard

Pages: 29 –38

[“Are wine reviewers reliable and consistent?” is always a big problem in wine review process. This chapter investigates the consistency of the wine being considered as “outstanding” or “extraordinary” using the dataset1: The Big dataset.]
Available

Regression in Wineinformatics

Chen, Bernard

Pages: 39 –44

[Regression analyses are applied on the big dataset to work on the question of “Can actual wine grade and price be predicted through their reviews?” This chapter provides an opportunity to discover the relationship between the exact wine grade and the combination of flavor, wine body and...
Available

Multi-Class, Multi-Label and Multi-Target in Wineinformatics

Chen, Bernard

Pages: 45 –55

[“Can wine grade, price and region being predicted altogether with higher accuracy?” In previous chapters, bi-class classification and regression were discussed in grade class prediction, evaluation of wine reviewers and price prediction independently; that is, each classifier was given one task...
Available

Advanced Usage of the Computational Wine Wheel

Chen, Bernard

Pages: 57 –65

[“How can computers understand wine reviews even more?” Since the Computational Wine Wheel has built-in hierarchy among attributes; this chapter wants to address how to extract even more information from wine reviews through the Computational Wine Wheel to represent CATEGORY and SUBCATEGORY...
Available

Conclusion and Future Works

Chen, Bernard

Pages: 67 –69

[In this book, Wineinformatics, a new data science application domain, was introduced systematically for the first time. Human language format wine reviews served as the main data source for this work. In order to accurately pre-process wine review data, the Computational Wine Wheel was utilized...

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