Wineinformatics

Wineinformatics is a new data science application with a focus on understanding wine through artificial intelligence. Thousands of new wine reviews are produced monthly, which benefits the understanding of wine through wine experts for winemakers and consumers. This book systematically investigates how to process human language format reviews and mine useful knowledge from a large volume of processed data. This book presents a human language processing tool named Computational Wine Wheel to process professional wine reviews and three novel Wineinformatics studies to analyze wine quality, price and reviewers. Through the lens of data science, the author demonstrates how the wine receives 90+ scores out of 100 points from Wine Spectator, how to predict a wine's specific grade and price through wine reviews and how to rank a group of wine reviewers. The book also shows the advanced application of the Computational Wine Wheel to capture more information hidden in wine reviews and the possibility of extending the wheel to coffee, tea beer, sake and liquors. This book targets computer scientists, data scientists and wine industrial researchers, who are interested in Wineinformatics. Senior data science undergraduate and graduate students may also benefit from this book.

Introduces a new and exciting data science application domain in wine Provides a comprehensive investigation in processing human language format reviews through the Computational Wine Wheel Demonstrates a systematic approach to evaluate and rank wine reviewers

Autorentext
Bernard Chen is currently a full professor and undergraduate coordinator of computer science department at University of Central Arkansas. He received his Ph.D. degree in computer science with bioinformatics concentration from Georgia State University in 2008. He is currently a full professor and undergraduate coordinator at the same department. He is the author or coauthor of approximately 80 papers in various interdisciplinary studies. In 2014, compared with existing data mining studies in wine works on approximately 100 wines at a time, he proposed a new data science application named Wineinformatics to analyze tens of thousands of wines through artificial intelligence. Since then, he has published eight journals and nine conference peer-reviewed papers directly related to Wineinformatics. He currently serves as a guest editor in the journal Fermentation for a special issue titled Machine Learning in Fermented Food and Beverages.

Inhalt
Chapter 1 Introduction.- Chapter 2 Data collection and preprocessing.- Chapter 3 Classification in Wineinformatics.- Chapter 4 Regression on Wine Prediction.- Chapter 5 Analysis on Wine Reviewers.- Chapter 6 Advanced Application of the Computational Wine Wheel.- Chapter 7 Conclusion and Future Works.