lenses, Machine Learning, Tasting, Tools, Wine Words
Comments 3

Models vs. Reality, Part 1

Wine appreciation requires language. But the way you use language depends on what you consider to be a “good tasting note.” What is good? What’s the norm?

View at Medium.com

…writing is a learned activity, no different in that regard from hitting a golf ball or playing the piano. Yes, some people naturally do it better than others. But apart from a few atypical autodidacts (who exist in all disciplines), there’s no practical way to learn to write, hit a golf ball, or play the piano without guidance on many points, large and small. And everyone, even the autodidact, requires considerable effort and practice in learning the norms. The norms are important even to those who ultimately break them to good effect. Bryan A. Garner, Garner’s Modern American Usage (2009, p. 104)


Read the full and updated post on Medium


  1. Very interesting stuff. Heuristically it looks like the PCA has done a nice job at clustering various words together– although the cassis/blackcurrant split is curious.

    How much of the variance is explained by each component? PC1 looks to me to be some kind of ripeness/body measure, with the greener wines at the start, moving through light Pinot-like wines, into the bruisers. Have you looked at how this component correlates with “traditional” wine measures (or is that to come in later posts)? I can’t immediately translate what PC2 might be, perhaps you have some insight into this.


    Liked by 1 person

    • Good questions! I think I’d now like to look into the cassis/blackcurrant split further. Do the critics who use “cassis” sometimes use “blackcurrant” in a different context to make a different kind of point? Or perhaps the split is explained by having 1-2 writers in the mix who don’t use “cassis” + their use of “blackcurrant” is different? The split isn’t huge, so I do think it’s curious rather than problematic… To answer your question: 22%, 15%, 12% (added a picture to the post). As to the interpretation of the components, I think that requires a lot more work. I like your interpretation of PC1. I think of these methods as providing tastes/flavours of large bodies of tasting notes. There’s a lot of information in the notes, so it’s interesting to look at different representations, through different lenses. Of course to the machine there’s no difference between vectorizations of wine notes and dance choreographies. But that’s the beauty of machine-assisted tasting note analysis: we may well be able to attach a meaning to “PC1” like you have done (we can also try to check/verify these hunches by looking at the underlying wines)… And I hope ultimately someone will consider these pictures as useful additions to standard tasting norms. It works best if you ask a targeted question like: ABC is my favourite critic, I’m tasting wine xyz give me a map of his/her descriptors related to “tannic” for Australian red wines. OK, now compare it to the same map for critic DEF… Let’s discuss further!

      Edit: Initial comment had a mistake (explained variance was not in proportion). Corrected.


      • Thanks! Agree with all your points. I think understanding this mapping from flavours to words is important, as it’s key in how people understanding wine, so exciting to see you are having a stab. I look forward to seeing the further analysis as it comes.

        Liked by 1 person

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