Applying Neural Networks in Wineinformatics with the New Computational Wine Wheel

Abstract

Wineinformatics involves the application of data science techniques to wine-related datasets generated during the grape growing, wine production, and wine evaluation processes. Its aim is to extract valuable insights that can benefit wine producers, distributors, and consumers. This study highlights the potential of neural networks as the most effective black-box classification algorithm in wineinformatics for analyzing wine reviews processed by the Computational Wine Wheel (CWW). Additionally, the paper provides a detailed overview of the enhancements made to the CWW and presents a thorough comparison between the latest version and its predecessors. In comparison to the highest accuracy results obtained in the latest research work utilizing an elite Bordeaux dataset, which achieved approximately 75% accuracy for Robert Parker’s reviews and 78% accuracy for the Wine Spectator’s reviews, the combination of neural networks and CWW3.0 consistently yields improved performance. Specifically, this combination achieves an accuracy of 82% for Robert Parker’s reviews and 86% for the Wine Spectator’s reviews on the elite Bordeaux dataset as well as a newly created dataset that contains more than 10,000 wines. The adoption of machine learning algorithms for wine reviews helps researchers understand more about quality wines by analyzing the end product and deconstructing the sensory attributes of the wine; this process is similar to reverse engineering in the context of wine to study and improve the winemaking techniques employed.