Do you ever get the feeling that wine critics are making up words or inventing fruit you’ve never heard of to describe wine? This morning, I took a sidestep in my computer-generated wine reviews project. Instead of generating whole reviews, I am now generating new words to describe wines. Here is a list of words that the computer generated to describe O’Vineyards Trah Lah Lah 2008. The hope is that they all sound vaguely real.
List of Computer Generated Wine Terms
To spice things up, today I’m highlighting computer generated words rather than whole reviews. This means the n gram analysis focuses on letter pairs and letter triplets instead of word pairs and word triplets. If you have no idea what I’m talking about, refer to the simplified explanation in my first post about computer generated reviews. Basically, the computer looks at what letters commonly appear together and it makes up words based on the statistical probability of random letters appearing near each other.
The list starts with words that strictly follow the analysis (high similarity to actual letter pairs in real reviews of Trah Lah Lah 2008) and it slowly descends into the bowels of vaguely human-sounding language (low similarity to actual letter pairs). All capitalization and punctuation was generated by the algorithm.
Perhaps of special interest, the computer generated the word “commend” even though that never appeared in the reviews. It also got a couple of real french words like “vraiment” and “cours”.
I definitely want to add some of these automatically generated words to my wine vocabulary. I wonder how long it will be before somebody calls me out for using made-up words like vinegativity, mell and bood.
This wine is quite differench. Extremendously bracked attack. Midpalate is dominated by gravinter with some notes of refunky vinegativity. Mell with a measive finish that reminds me of cracket cherritory.
I’m still tweaking the parameters for my computer-generated wine reviews.
Some computer-generated reviews:
“Delicious, deep flavours.”
While this is in no way funny, it’s sort of spectacular. Nobody actually used this exact phrase in the wine reviews. But somebody said “Delicious, deep and dusty. It should cost more.” And somebody else said “Rich deep flavours and a long finish.” And the computer sussed out that it could say “Delicious, deep flavours.” It even got the punctuation and capitalization correct. It’s fun to focus on the zaniest reviews the computer generates. But some of these boring ones are actually much more impressive.
“really, really solid quaffing red. It tastes True again. Nice wines. Thanks again. Good effort”
I like this one for all the reasons mentioned above. The simple parts are remarkably accurate. And the note that a wine tastes True again is amazing. You could actually get away with saying that in a review. Although I think if I had a greater respect for line breaks, there would have been a big gap betweent tastes and True. I’ll look into that.
“The 2008 Trah Lah Lah Lah Lah Lah Lah. No, sorry.”
Lest you think the computer only generates positive reviews of my wine… 😀 Aside from being a hilariously curt negative review, this also demonstrates one of the most amazing things about recursive analysis. My wine is called Trah Lah Lah. So the computer has about a 50/50 chance of saying the whole brand name any time it decides to say Trah. Trah is always followed by Lah. And Lah is followed by Lah about half the time. And by a period or another word about half the time. So you see a lot of Trah Lah and a lot of Trah Lah Lah in the generated reviews. But occasionally, you get lucky and the computer just strings together a ton of Lah Lah’s. If I were using trigrams, this probably wouldn’t happen as often. But for now, here we are. And actually, in this particular negative review, it sounds like they’re making fun of the name of the wine.. so it’s perfect!
“Gorgeous fruity New World Wines, with their ‘old fashioned’ flavour”
Program I used
I’m using Gibberizer for now. I might write something on my own later, but for now it’s all thanks to this beauty: http://code.google.com/p/gibberizer/
The settings are
- Read input as: Lines
- BatchSize: 1
- Similarity: 7
- Persistence: 5
- Disallow input echo
- Disallow duplicates
What changed since the last post?
If you read the last post on this subject, you’ll probably notice that these reviews are much more sensical. So what’s different?
First and foremost, I changed the data input. Instead of feeding the last 100 comments I received on Naked Wines, I submitted only tasting notes for the 2008 Trah Lah Lah. That’s 113 reviews. They tend to be a little shorter than comments, so the data file is about the same length, but all the language is about drinking wine. This means that the computer generates fewer comments about technical aspects of the website like the MarketPlace and the vineshare program we’re running.
Don’t get me wrong. I’d love to generate comments of that nature too. But I just need way more data for that to work. Tasting notes are easier because even the real ones sound a bit like gibberish… and people often get so drunk while tasting the wine that the reviews tend to be a bit slurred by the end.
I should also mention that lots of the reviews are still total gibberish.. for example:
“A bit tannins as well. As a Rhode Islander to breathing” for a good 🙂 will buy again.”
Work in progress!
“Drank lots and lots of depth, it won’t disappoint”
–computer generated review of O’Vineyards wine
I’m playing with some software that will allow me to analyze all the comments O’Vineyards wines have received online. One of the sillier, fun applications of this analysis is that my computer can generate comments on its own now. 😀
Some of you might be familiar with the silly tasting note generator or similar sites, but these use slightly different technology. I’m working with n-gram analyses of the reviews I get from Naked Wines customers.
What is an n gram analysis?
Basically, the computer counts every word and then it counts every word pair and then it counts every word triplet and so on. This data lets the computer draw some conclusions about what words tend to appear together. So if I do an n-gram analysis of the phrase “I went to the movies”, the word pairs are:
- X I
- I went
- went to
- to the
- the movies
- movies X
The X’s indicate the start or end of a phrase.
The word triplets in the same phrase would be
- X I went
- I went to
- went to the
- to the movies
- the movies X
How does the computer generate new sentences?
The more data you feed into the computer, the more n-grams it collects. And it can eventually draw some relatively accurate conclusions. Imagine if I do a larger sentence like “I went to the movies and had to wait in the longest line ever to buy some popcorn”, the program would notice all the previous word pairs as well as the new pair: “to buy”… and the computer might conclude that it’s normal to say “I went to buy some popcorn.” and that is actually correct! Of course a lot of the time, the computer tries hard but just spouts gibberish. Like “I went to the longest popcorn ever to buy some movies.”
This differs from the silly tasting note generator mentioned above because that generator works more like a mad lib. It has long lists of words that are manually categorized as modifiers, nouns, verbs, or other parts of speech, and it uses pre-written sentence structures. It makes more sense given very little data, but it is limited to what it has been taught. What I’m working on could eventually be applied to any body of letters (even a language I don’t speak) and generate reviews based on an n gram analysis of that text (so I could do this for Japanese reviews even though I don’t even speak Japanese!)
Most of the time, the computer generated reviews are total gibberish. The syntax can be terribly wrong. Here are some fun examples of typical gibberish reviews:
This is a very good black-red with onions, sauteed pots with our Les American than Languedoc and complicated, dark fruitiness notes, but this achieved the lower they called it loved it. Even my 81 year when the wine, we had to open the duration is elastic, then essentially the oven as it needs taste when the tasted some mixed cases now decreased to say about to email the sale this bottle
lot of purple. Very floral with the market Place right-hand drive!
Got through fruit and Joe are in the minimum quantity !
Wouldn’t spoil something else on my anatomy. I do buy wine is not in favour of the buyer, and less fun!
I found it was a please passed over the price and my guests both gave it 5 out on it! I really want it?
Big (not one a couple of days when we got back?
Almost there are dark plum tang and can under for anything wrong with Sunday lunch – open the last remnants post-food start to see how this aspect of the price, in recent trip to Carcassonne and price.
If you’re looking to hear your tounge without food and you at the Trah Lah Lah Lah was reminiscent of view it is dashed good! Which we found it interesting last remnants post-food start to show the silly name it’s frigging fantastic price. Remember if it was a 2008 or 2009 vintage) compares to taking decanted, and do under for anything. I was very intense, a good time favourite of the best wishes for a while to get the two, i sense a marketplace (the 2006 is supposed to be missing out of 5 others one not to everyone (that’s just slid down and Joe) may be more than a Merlot) Cabernet blend, or from her tasting and it was subtle and give the producer an enjoyed this is due to financial constraints, and you are missing out of 5 others one changing to see how those 5’s ! :)”
It’s clear that the words are related to wine (and the computer does manage to group brand names like Trah Lah Lah, and mention my region, vintages, and other things that make this sound like a tasting note). So it sounds like English. But then when you actually look at the whole paragraph, there’s no sense at all! 😀
Sometimes though, the gibberish words line up just right and there’s a strange sort of wisdom in the computer’s misuse of the English language.
Hi Sandy, you get what you pay for what does she know ha ha ha.
Swirl it intense, a good with the yanks in men, what I had, but the wine, but not quite quick). This wine front of her, was an open it was quite French.
I have bid? – I thinking wine. Rich and can understand the base proposition of those tannin heavy so a good with food… Lamb medallions, sauteed pots with onions, snow peas, and body from naked wines and as we worked our way throughout our stay. Ryan and dirty with food… Lamb medallions, sauteed pots with the seller can extending that basis I have order, you wanted us to the extra years in the vineyard and as always the sale this remarkable wine in the front of parma violets are they used to make a lot of purple. Very floral smell of Lilies and lots of flavour packed the grapes and Edinburgh and less fun!
Ya, I still need to work on it.
Totally unrelated to wine
Sometimes, the reviews seem totally unrelated to wine!
I found as always the last night.
Big (not one a couple of days when we got back?
I’d been toying it!
Why the heck am I doing this?
If you know me at all, you really should get used to me doing strange stuff all the time. But there is actually a reason for this. It’s raining outside and the paint is drying in B&B room #3 (codename: the Cabardes Room). So it’s a perfect opportunity to further my research in data visualization and analysis. I’m going to try to broach this subject with my technical audiences much more often in 2012 (including but not limited to a potential SxSW talk on data analysis for non-verbal experiences like wine drinking).
I want to share another trend in wine journalism that has piqued my curiosity: nonverbal wine reviews. Talking about wine without words. If you think any of the following are cool, please review my wines without words! Or review some of your favorite wines nonverbally. You don’t even need a blog since you can use sites like Petrogasm to post your own wordless reviews.
The prime example that has me thinking about this all the time is Chateau Petrograsm, a blog where anybody can register and review a wine by posting a picture. You don’t get to explain why the photo is representative of the wine. “Readers” must use their imaginations to connect the dots between the picture and the wine being reviewed.
Sometimes, it’s fairly obvious. A picture of crisp golden apples because the wine reminded the reviewer of crisp golden apples. Other times, it’s less obvious. People will sometimes pick a celebrity whose character matches the wine. Or they’ll pick a landscape that is very complicated and almost as nuanced as the wine itself. Here, a new user characterizes a wine from La Negly with a dark and brooding coastline. These reviews are often less judgmental and nitpicky than verbal reviews.
I used this photo to the left for a wine I tasted once. I don’t want to explain my choice because I think that ruins the fun and gets unnecessarily intellectual. But suffice it to say that a photo like this can give multiple and almost conflicting images at the same time. And that’s how taste works sometimes.
By getting away from words and going back to a more symbolic review of wine, we free ourselves from the tyranny of language and expectations!
I just really love the concept and I think it reminds us that some pictures are worth far more than 1,000 words.
I very frequently feel like I’m at a total loss for words when talking about wine in a foreign language. And hell, even in my native language, a wine can have so many apparently opposite traits at the same time! It’s hard to talk about it naturally. The reason I rely so much on video in my wine reviews at Love That Languedoc is because I think you can easily convey a ton of information through body language and spastic hand gestures. Sometimes, it would take far longer to convey the same information in words.
And I’m not alone. Right, Gary?
Gary Vaynerchuk is a guy who talks with his whole body when he reviews a wine. And it’s great. Because he can deliver a lot of surprisingly nuanced descriptions with a little nudge of his shoulder or by throwing his hands up in the air as he talks about different layers of a wine.
And occasionally you can spawn catch phrases that go along with a little body language and then you make tshirts and the whole nine yards. Oak Monster!
Music and Wine
I’ve also seen a few efforts to pair music and wine. Some of these music and wine pairing attempts actually do get very wordy. I know I commented on a blog the other day that did this with much less pretense, but I can’t find it!! The author would just post the title of a song at the top of the wine review. And it begs the question, does he think the wine is like that song or that the wine pairs with that song? After all, some people swear that a wine’s quality can change drastically based on what music you listen to.
And it might be interesting to see if there’s a site that tries to actually embed audio so that it can truly be nonverbal. BottleDJ is a blogger who tries to pair music and wine to interesting effects.
We were actually talking about a similar concept at VinoCamp Paris. Wouldn’t it be cool if there were a website like last.fm that looked at your musical tastes and predicted what kind of wines you’d enjoy? I wonder how effective it would be!
What music does O’Vineyards go with or what songs does it remind you of?! TELL ME.