Lefever, Els, Hendrickx, Iris, Croijmans, Ilja et al. (2 more authors) (2018) Discovering the Language of Wine Reviews: A Text Mining Account. In: Calzolari, Nicoletta, Choukri, Khalid, Cieri, Christopher, Declerck, Thierry, Goggi, Sara, Hasida, Koiti, Isahara, Hitoshi, Maegaard, Bente, Mariani, Joseph, Mazo, Hélène, Moreno, Asuncion, Odijk, Jan, Piperidis, Stelio and Tokunaga, Takenobu, (eds.) Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). LREC , Miyazaki,Japan , pp. 3297-3302.
Abstract
It is widely held that smells and flavors are impossible to put into words. In this paper we test this claim by seeking predictive patterns in wine reviews, which ostensibly aim to provide guides to perceptual content. Wine reviews have previously been critiqued as random and meaningless. We collected an English corpus of wine reviews with their structured metadata, and applied machine learning techniques to automatically predict the winetextquotesingles color, grape variety, and country of origin. To train the three supervised classifiers, three different information sources were incorporated: lexical bag-of-words features, domain-specific terminology features, and semantic word embedding features. In addition, using regression analysis we investigated basic review properties, i.e., review length, average word length, and their relationship to the scalar values of price and review score. Our results show that wine experts do share a common vocabulary to describe wines and they use this in a consistent way, which makes it possible to automatically predict wine characteristics based on the review text alone. This means that odors and flavors may be more expressible in language than typically acknowledged.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2018, the European Language Resources Association. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Psychology (York) |
Depositing User: | Pure (York) |
Date Deposited: | 16 Oct 2018 10:20 |
Last Modified: | 16 Feb 2025 00:05 |
Status: | Published |
Publisher: | LREC |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:137244 |