Hostname: page-component-6bb9c88b65-dwch4 Total loading time: 0 Render date: 2025-07-23T22:23:41.015Z Has data issue: false hasContentIssue false
  • English
  • Français

Expensive and Cheap Wine Words Revisited

Published online by Cambridge University Press:  18 February 2022

Kevin W. Capehart Open the ORCID record for Kevin W. Capehart [Opens in a new window]
Kevin W. Capehart*
Affiliation:
Department of Economics, California State University, Fresno, 5245 N Backer Ave M/S PB20, Fresno, CA 93740; e-mail: kcapehart@csufresno.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

Previous work has quantitatively analyzed expert wine descriptions to identify some so-called “expensive” and “cheap” words that are indicative of a wine's price. This paper revisits that work. In particular, I examine whether words previously identified as expensive and cheap ones are still indicative of a wine's price when using the same methods and a different, larger dataset. My findings mostly confirm previous conclusions, although many directions for further research into expensive and cheap wine words remain open. (JEL Classifications: D83, L66)

Keywords

expert evaluationwine

Information

Type
Articles
Information
Journal of Wine Economics , Volume 16 , Issue 4 , November 2021 , pp. 411 - 418
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of American Association of Wine Economists

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

*

I thank two anonymous reviewers and Karl Storchmann, the editor of the journal.

References

Barth, J., Katumullage, D., Yang, C., and Cao, J. (2021). Classification of wines using principal component analysis. Journal of Wine Economics, 16(1), 56–67.Google Scholar
Buccafusco, C., Masur, J. S., and Whalen, R. (2021). How many Latours is too many? Measuring brand name congestion in Bordeaux wine. Journal of Wine Economics, 16(4), 419–428.CrossRefGoogle Scholar
Capehart, K. W. (2021). Willingness to pay for wine bullshit: Some new estimates. Journal of Wine Economics, 16(3), 260–282.Google Scholar
Chen, K. J., and McCluskey, J. (2018). Impacts of expert information on prices for an experience good across product segments: Tasting notes and wine prices. Journal of Agricultural and Resource Economics, 43(3), 388–402.Google Scholar
Croijmans, I., Hendrickx, I., Lefever, E., Majid, A., and Van Den Bosch, A. (2020). Uncovering the language of wine experts. Natural Language Engineering, 26(5), 511–530.CrossRefGoogle Scholar
Dong, Z., Guo, X., Rajana, S., and Chen, B. (2020). Understanding 21st century Bordeaux wines from wine reviews using Naive Bayes classifier. Beverages, 6(1), 5.Google Scholar
The Economist. (2011). Describing wine: Don't let's whine. March 3. Available at https://www.economist.com/johnson/2011/03/03/dont-lets-whine (accessed March 3, 2011).Google Scholar
Gentzkow, M., Kelly, B., and Taddy, M. (2019). Text as data. Journal of Economic Literature, 57(3), 535–574.CrossRefGoogle Scholar
Hovy, H., Melumad, S., and Inman, J. (2021). Wordify: A tool for discovering and differentiating consumer vocabularies. Journal of Consumer Research, 48(3), 394–414.Google Scholar
Klimmek, M. (2013). On the information content of wine notes: Some new algorithms? Journal of Wine Economics, 8(3), 318–334.Google Scholar
Krumme, C. (2009). A nose by any other name: Descriptors as signals for wine price. Paper presented at the 3rd Annual Conference of the American Association of Wine Economists in Reims, France, June 18–20. Available at https://pdfs.semanticscholar.org/9299/b521e4720b92f9028b9b7d03da8a1a54256b.pdf (accessed June 1, 2018).Google Scholar
Krumme, C. (2011). Velvety chocolate with a silky ruby finish, pair with shellfish. Slate, February 23. Available at http://www.slate.com/articles/life/drink/2011/02/velvety_chocolate_with_a_silky_ruby_finish_pair_with_shellfish.html (accessed February 23, 2011).Google Scholar
Krumme, C. (2013). Volatile elements: The dynamics of wine descriptors from 2001–2011. Paper presented at the 7th Annual Conference of the American Association of Wine Economists in Stellenbosch, South Africa, June 26–29. Available at https://www.wine-economics.org/wp-content/uploads/2013/07/coco_krumme.pdf (accessed June 1, 2018).Google Scholar
McCannon, B. C. (2020). Wine descriptions provide information: A text analysis. Journal of Wine Economics, 15(1), 71–94.Google Scholar
Metsis, V., Androutsopoulos, I., and Paliouras, G. (2006). Spam filtering with Naive Bayes: Which Naive Bayes? Proceedings of the 2006 Conference on Email and Anti-Spam. Available at https://www.ceas.cc/2006/15.pdf (accessed August 1, 2021).Google Scholar
Niklas, B., and Rinke, W. (2020). Pricing models for German wine: Hedonic regression vs. machine learning. Journal of Wine Economics, 15(3), 284–311.CrossRefGoogle Scholar
Petrocelli, J. V. (2021). The Life-Changing Science of Detecting Bullshit. New York, NY: St. Martin's Press.Google Scholar
Ramirez, C. D. (2010). Do tasting notes add value? Evidence from Napa wines. Journal of Wine Economics, 5(1), 143–163.CrossRefGoogle Scholar
Thoutt, Z. (2017). Wine reviews. Available at https://www.kaggle.com/zynicide/wine-reviews (accessed July 1, 2021).Google Scholar
Yang, C., Barth, J., Katumullage, D., and Cao, J. (2022). Wine review descriptors as quality predictors. Evidence from language processing techniques. Journal of Wine Economics, forthcoming.Google Scholar
Supplementary material: File

Capehart supplementary material

Capehart supplementary material

Download Capehart supplementary material(File)
File 43.8 KB