Using Neural Network Models for Wine Review Classification

Abstract

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)

Applying Neural Networks in Wineinformatics with the New Computational Wine Wheel

“…These attributes offer valuable insights into the characteristics that contribute to positive and negative instances, which are particularly relevant for the wine industry, especially during the fermentation process, as most of the wine's features are determined during this stage. Recently, more and more studies are working on analyzing the relationship between wine reviews and quality [16,[30][31][32]; it is crucial to note that evaluation metrics such as accuracy are simply providing numbers for preliminary results, discovering useful knowledge that might be more important and which humans can understand is the final goal. 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 in order to study improve the winemaking techniques employed.…”

“…Commonly used examples of black-box models are hyperplane-based models, such as support vector machines [13], which use subspaces to separate the problem's classes. A neural network is another popular black-box classification algorithm, which simulates brain neurons in many different wine-related studies including taste sensors [14], electronic nose [15], and wine review analysis [16]. In contrast, white-box models, also known as explainable models, are based on patterns or rules that can be easily understood and explained in practical applications.…”

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“…Passion for wines has grown throughout the world over the years, as have the methods for processing and combining grapes, which continue to lead to improvements in various products. As a result, several researchers have conducted studies on the cultivation method (Xu et al 2020), grape varieties, and classi cation between geographical regions (Costa et al 2018; Katumullage et al 2022).…”

“…One way to determine the authenticity of the samples is through chemical evaluation and data analysis to compare, analyze and classify the samples. In this regard, many studies provide a literature review (Gabrielli et Although these studies are critical and play a signi cant role, reading text reviews can be timeconsuming, especially when there are many similar documents (Katumullage et al 2022). With the explosion of research literature production, the need for new approaches to knowledge structuring emerged (Kokol et al 2021).…”