Vivino is a very popular wine app containing millions of wines. Many wines have over 100k+ reviews!
I noticed approximately 0.5% (~1 in 200) of reviews contain the price a user paid and their sentiment for that price. An example of reviews with “price-sentiment” are shown in the diagram below.
My idea is to crowdsource price-sentiments to judge whether the price of wine set by my liquor store is good or not.
For example, suppose the LCBO (my liquor store) sells a particular bottle for $13.99. I look at all of Vivino’s reviews for the bottle and find users who:
This requires a large number of reviews to:
So, I scraped thousands of wine products from the LCBO website! I matched them to bottles on Vivino and gently downloaded about 6 million corresponding reviews. Both the LCBO and Vivino datasets are hosted on my local PostgreSQL database for fast querying.
I continued to gently amass 31 million reviews. This blog looks at the first partition of tables consisting of 11M reviews.
import pandas as pd
q = """
SELECT COUNT(*) as count
FROM vivino
GROUP BY vivino_id
;
"""
numReviews = sum(pd.read_sql(q, sql_address)['count'])
numWines = len(pd.read_sql(q, sql_address)['count'])
print(f"Total number of reviews: {numReviews}")
print(f"Total number of wines: {numWines}")Total number of reviews: 11214793
Total number of wines: 12845import pandas as pd
q = """
SELECT COUNT(*) as count
FROM vivino_extra
GROUP BY vivino_id
;
"""
numReviews = sum(pd.read_sql(q, sql_address)['count'])
numWines = len(pd.read_sql(q, sql_address)['count'])
print("Note: From extra table not used in this analysis:")
print(f"Total number of reviews: {numReviews}")
print(f"Total number of wines: {numWines}")Note: From extra table not used in this analysis:
Total number of reviews: 20134847
Total number of wines: 49517from IPython.display import Markdown
from tabulate import tabulate
q = """
SELECT column_name
FROM information_schema.columns
WHERE table_schema = 'public'
AND table_name = 'vivino'
;
"""
colNames = pd.read_sql(q, sql_address)['column_name']
ncolHalf = int(len(colNames)/2)
Markdown(tabulate(
list(zip(colNames[0:ncolHalf], colNames[ncolHalf:])),
headers=["Feature names","Feature names (cont'd)"]
))| Feature names | Feature names (cont’d) |
|---|---|
| review_id | user_id |
| vivino_id | user_seo_name |
| rating | is_featured |
| review | is_premium |
| language | user_followers_count |
| created_at | user_followings_count |
| likes_count | user_ratings_count |
| comments_count | user_ratings_sum |
| flavors | user_reviews_count |
Here is a histogram of all star ratings. Most votes are in 0.5 increments (e.g., 3.5, 4.0, 4.5). This is because the app had permitted users to only rate in 0.5-star increments.
import matplotlib.pyplot as plt
q = """
SELECT rating
FROM vivino
;
"""
tbl = pd.read_sql(q, sql_address)
plt.hist(tbl['rating'], bins=81)
plt.xlabel('Star Rating')
plt.ylabel('Count (millions)')
plt.title('Distribution of Star Ratings')
plt.xlim(0.95,5.05)
plt.show()
There are reviews with low ratings! Here is a log-y plot.
plt.hist(tbl['rating'], bins=81, log=True)
plt.xlabel('Star Rating')
plt.ylabel('Count')
plt.title('Distribution of Star Ratings')
plt.ylim(0.9, 1e7)
plt.xlim(0.95,5.05)
plt.show()
Let’s integrate the histogram to get a cumulative distribution.
plt.hist(tbl['rating'], bins=81, cumulative=True, density=True)
plt.xlabel('Star Rating')
plt.ylabel('Normalized Density')
plt.title('Cumulative Distribution of Star Ratings')
plt.xlim(0.95,5.05)
plt.show()
The above plots clearly show the ratings are distributed in a highly irregular fashion.
Looks like less than 5% of bottles are rated below 3-stars. Why?
The answer is likely some combination of theories 1, 3, and 5. For instance, I would not expect many true 1-star wines to make it to market due to severe costs of production and self-destructive branding. Rather, 1-star ratings are likely from faulty/damaged bottling or storage, peculiar drinking preferences, or splitting1.
Wine Spectator’s 100-point Scale gives credence to Theory 3. Anything below 50-pts or 3-stars is “Not recommended” and would be a devastating review.
Note: I had no impression of Theory 4 until I dug a tiny bit deeper. There are a LOT of inexplicable behaviour that would surprise me if they were human.
The distribution of the average rating per wine shows a nice demonstration of the Central Limit Theorem.
from scipy.optimize import curve_fit
import numpy as np
def gaussian(x, mu, sigma, a):
return a*np.exp(-0.5*(x-mu)**2/sigma**2)
q = """
SELECT AVG(rating) as avg_rating
FROM vivino
GROUP BY vivino_id
;
"""
tbl = pd.read_sql(q, sql_address)
h = plt.hist(tbl['avg_rating'], bins=81)
xdata = h[1][1:] - 0.5*np.diff(h[1])[0] #get center of edges.
ydata = h[0]
popt, pcov = curve_fit(gaussian, xdata, ydata, p0=(4, 0.5, 100))
plt.plot(xdata, gaussian(xdata, *popt),
'r-',
label=r'Gaussian: $\mu$=%5.3f, $\sigma$=%5.3f' % tuple(popt[0:2]))
plt.xlabel('Star Rating')
plt.ylabel('Count')
plt.title('Cumulative Distribution of Star Ratings')
plt.xlim(0.95,5.05)
plt.legend()
plt.show()
Let’s look at the distribution of reviews per wine bottle. I use log-binning to capture the granularity of wines with few reviews.
A log-normal distribution with a bit of skewness is a good description. These distributions appear normal when the x-axis is logscale.
q = """
SELECT count(*) as count
FROM vivino
GROUP BY vivino_id
;
"""
tbl = pd.read_sql(q, sql_address)
def lognormal(x, mu, sigma, a):
return a*np.exp(-0.5*(np.log(x)-mu)**2/sigma**2) / (x * sigma)
h = plt.hist(tbl['count'], bins=np.logspace(np.log10(1),5, 100))
xdata = 10**(np.log10(h[1])[1:] - 0.5*np.diff(np.log10(h[1]))[0])
ydata = h[0]
popt, pcov = curve_fit(lognormal, xdata, ydata, p0=(250, 50, 100))
plt.plot(xdata, lognormal(xdata, *popt),
'r-',
label=r'Log-Normal: $\mu$=%5.3f, $\sigma$=%5.3f' % tuple(popt[0:2]))
plt.xlabel('Number of reviews per wine')
plt.ylabel('Count')
plt.xscale('log')
plt.title(f'Distribution of Reviews per Wine')
plt.show()
Most wines have between 50 to 500 reviews each. The peak is at ~250 reviews per bottle.
Earlier, I stated approximately 0.5% or 1-in-200 of reviews have price-sentiments (i.e., the reviews that I want). Let’s say we need 10 price-sentiments to have an interesting signal. Then only bottles with 2,000+ reviews will likely the signal I desire. From the cumulative distribution below, that is approximately 15% of bottles.
h = plt.hist(tbl['count'], bins=np.logspace(np.log10(1),5, 100), cumulative=True, density=True)
plt.xlabel('Number of reviews per wine')
plt.ylabel('Cumulative number of wines')
plt.xscale('log')
plt.title(f'Cumulative Distribution of Reviews per Wine')
plt.axvline(2000, c = 'r')
plt.show()
The above figure is for the entire dataset which includes wines I am not able to purchase at my liquor store. If I exclude those wines, I find approximately 450 bottles purchasable at our liquor stores have over 2,000 reviews. This suggests there are plenty of purchasable wines to build a wine recommendation app based on price-sentiment.
q = """
SELECT count(*) FROM (
SELECT vivid2 FROM (
SELECT sku, vivid2 FROM index_matches
WHERE sku IS NOT null
AND vivid1 IS NOT null
AND vivid2 IS NOT null
) AS matches
INNER JOIN (
SELECT sku FROM inventory
GROUP BY sku
) AS buyable
ON matches.sku = buyable.sku
) AS buyable
LEFT JOIN (
SELECT vivino_id FROM vivino
) AS vivino_reviews
ON vivid2 = vivino_id
GROUP BY vivino_id
"""
tbl = pd.read_sql(q, sql_address)
h = plt.hist(tbl['count'], bins=np.logspace(np.log10(1),5, 100), cumulative=True)
plt.xlabel('Number of reviews per wine')
plt.ylabel('Cumulative number of wines')
plt.xscale('log')
plt.title(f'Cumulative Distribution of Reviews per Wine (Purchasable at the LCBO)')
plt.axvline(2000, c = 'r')
plt.show()
print(f'There are {len(tbl)} bottles available at the LCBO.')
There are 1455 bottles available at the LCBO.Our next goal is to study the review strings such as the word and character lengths. Let’s grab a random set of 1 million reviews.
import re
q = """
SELECT review_id, review, language FROM vivino
ORDER BY RANDOM()
LIMIT 1000000
"""
tbl = pd.read_sql(q, sql_address)
#delete more than one whitespace
tbl['review'] = tbl['review'].apply(lambda x: re.sub(' +', ' ', x))import matplotlib.pyplot as plt
tbl['charLength'] = tbl['review'].apply(len)
plt.hist(tbl['charLength'], bins=int(max(tbl['charLength'])/2), log = True)
plt.title('Distribution of Character Lengths per Review')
plt.ylabel('Number of reviews')
plt.xlabel('Number of characters')
plt.ylim(1e2, 5e4)
plt.show()
print('Example of a 500+ character review:')
print(tbl[tbl['charLength']>=500]['review'].iloc[10])
Example of a 500+ character review:
👁 Garnet red
👃 Cherry, raspberries and oak, the freshness is also very intense.
👅 Light body, well balanced, medium finish and very soft aftertaste, I didn’t feel the acidity which is characteristic of the Pinot Noirs, which gave me a good impresion of this wine!
**Esp
👁 Rojo granate
👃 Cereza, frambuesa y roble, tambien senti una frescura intensa agradable
👅 Cuerpo ligero, bien equilibrado, final medio y retrogusto suave. No senti la acidez caracteristica de los Pinot Noir, lo cual me agrado bastante!
Most users express write fewer than 100 characters. The users who express more appear as a spike in the distribution due to the 520 character limit. Very long reviews have a number of sentences and points. They often contain a story, lists of taste, smell, and sight descriptors, or common wine nomenclature (e.g., tannins, body, acidity).
tbl['wordLength'] = tbl['review'].apply(lambda x: len(x.split(' ')))
plt.hist(tbl['wordLength'], bins=int(max(tbl['wordLength'] )), log = True)
plt.title('Frequency of words per Review')
plt.ylabel('Number of reviews')
plt.xlabel('Number of words')
plt.axvline(520/5.79, c = 'k', lw=1, label = "4.79 char. per word")
plt.legend()
plt.show()
A study of google books found that the average number of characters per word is 4.79 (Norvig). White spaces are counted in the character limit of reviews, so the average word approximately occupies 5.79 characters. The maximum number of average words in 520 characters is then 520 / 5.79 ~ 90 words, which is located at the knee in the distribution!
Let’s plot see how the character and word lengths are distributed together.
plt.scatter(tbl['charLength'], tbl['wordLength'], s= 1, alpha=0.5, label='language: any')
plt.scatter(tbl[tbl['language']==ja]['charLength'], tbl[tbl['language']==ja]['wordLength'], c = 'r', s= 1, alpha=0.5, label='language: ja')
plt.title('Scatter Plot of Word and Character Lengths')
plt.xlabel('Number of characters')
plt.ylabel('Number of words')
plt.plot([0,520], [0, 520/5.79], c = 'k', label = "4.79 char. per word")
plt.legend()
plt.show()
There are clearly two sets of distributions. The dominant diagonol distribution shows most reviews contain 4.79 characters (plus 1 white space) per word, no matter the review length!
The smaller distribution of reviews with a very high character-word density are either written in kanji, have numerous emojis, or are just riiiiiiiiiiiiiiiiiiidiculously long:
28362 ソライア2009\nTENUTA TIGNANELLO (ANTINORI)\nテヌータ・テ...
34597 Para comprar a cajas....\n\n\n👏👏👏👏👏👏👏👏👏👏👏👏👏\n🍷...
146622 ブレッドアンドバター、ピノノワール2021年\n\nカリフォルニア、ナパヴァレーの人気ワイナ...
166770 Graaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa...
222111 Graaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa...
234564 Graaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa...
245968 SONOMA COAST LES NOISETIERS CHARDONNAY 2019\n\...
271508 3,75*\n\nBuena RCP\n\n\n👏👏👏👏👏👏👏👏👏👏👏👏👏\n🍷🍷🍷🍷🍷🍷🍷...
306546 Graaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa...
320458 Graaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa...
324681 サヴォワ・アプルモン・キュヴェ・ガストロノミー 2019\n\n淡いライトグリーン。ゆったり...
335071 持って来てくれた人曰くオレンジワインらしい。\n\n外観はオレンジ色がかった強目の黄色からゴ...
394920 ユニオン・デ・グラン・クリュ・ド・ボルドーにて。\n2019ヴィンテージ。\n\n去年の20...
400628 グラハム・ベック・ブリュット・ロゼ NV\n\n少し紫がかった濃いピンク。決め細かな泡立ち。...
401848 シャトー アンジェリュス\n2018.2017.2014\n\n2018、濃い紫寄りのガーネ...
504967 自宅にて開栓。\n2015ヴィンテージ。\n\n家で2016ヴィンテージを飲んで気に入ったシ...
542056 T持ち込みワイン会にて。\n2015ヴィンテージ。\n\n自分が持ち込みブラインドで提供した...
559185 Graaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa...
610415 Bic Camera¥3680\nワインショップの店員さんのお話を聞いて、ネットや雑誌では知...
614611 Graaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa...
Name: review, dtype: objectHmm… There’s one user who enjoys writing reviews with very low information density.
tmpID = 355128697
q = f"""
SELECT count(*)
FROM vivino
WHERE review = (
SELECT review
FROM vivino
WHERE review_id = {tmpID}
)
AND user_seo_name='francisco_silva6'
--the name condition makes no difference
"""
tbl_gra = pd.read_sql(q, sql_address)
print(f'Number of duplicates: {tbl_gra["count"].iloc[0]}')
tbl[tbl['review_id']==tmpID]['review'].iloc[0]Number of duplicates: 131'Graaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaande vinho'Wow. 131 duplicates! Lots of questions. This is a good opportunity to talk about Vivino’s users.
Here are histograms for the number of likes and comments for a review.
import math
q = """
SELECT likes_count, comments_count
FROM vivino
;
"""
tbl = pd.read_sql(q, sql_address)
binWidth = 20
maxBinsLikes = int(math.ceil(max(tbl['likes_count'])/binWidth)+1)*binWidth
maxBinsComments = int(math.ceil(max(tbl['comments_count'])/binWidth)+1)*binWidth
rangeBinsLikes = range(0,maxBinsLikes, binWidth)
rangeBinsComments = range(0,maxBinsComments, binWidth)
plt.hist(tbl['likes_count'], bins=np.logspace(np.log10(1),4, 100), log=True, label="Likes")
plt.hist(tbl['comments_count'], bins=np.logspace(np.log10(1),4, 100), log=True, alpha=0.5, color = 'r', label="Comments")
plt.xlabel('Number of Likes, and Comments')
plt.ylabel('Log Count')
plt.title('Distribution of a Review Likes and Comments')
plt.xscale('log')
plt.legend()
plt.show()
From experience, it makes sense that there are fewer comments than likes2 and that most reviews do not have many likes. There are ~100 reviews that have over 500 likes, which is impressive. Here are a few samples:
Splendid Umbrian blend of 70% Merlot, 20% CS and 10% CF, originating from vineyards located on the Spello and Assisi hills, aged for 12m in French barriques and another 24m in the bottle.
Inky dark red, displaying scents of blueberries, blackberries, laurel, sweet almonds, smoky cedar and bitter dark chocolate refined with a whiff of mint.
Despite its age still very grippy, showing firm tannins that structure a dense core of fresh and juicy dark fruit with clear hints of pepper in the finisch.
4.0*
--------------------------------
⭐️ 90 P/ 4.0* Very nice aged Tawny Port. Typical for the style, with a good complexity and concentration. Can stand alone or with food (e.g. cheese).
👁 Pale tawny.
👃 Intense nose of tertiary flavors – raisins, tobacco, leather, cedar, roasted nuts, milk chocolate, butterscotch, rum infused berries, dates, cooked red and black berries.
👅 Half-sweet with a med+ acidity. High in alcohol with med and ripe tannins. The body is med+. The finish is dominated by fruity-tertiary notes and is med+ long.
--------------------------------
Merlot 55%+ syrah 35%+sangiovese 10%
Tonneau 12 months
At nose is quite complex: blackberry, blueberry, vanilla, cocoa, red flowers.
Rich tannins and good texture in mouth.
Good persistency with licorice notes.
--------------------------------It is shocking that there are reviews with more than a few hundred comments! That sounds busier than a controversial tweet. How can a review have over 2000 or 5000 comments?? Let’s take a peek.
q = """
SELECT comments_count as comments, likes_count as likes, review, user_seo_name as name, user_followers_count as followers, user_followings_count as followings
FROM vivino
WHERE comments_count > 400
ORDER BY comments_count
;
"""
tbl = pd.read_sql(q, sql_address)
tbl| comments | likes | review | name | followers | followings | |
|---|---|---|---|---|---|---|
| 0 | 405 | 205 | They say you will always remember your first, ... | kelley_do | 5997 | 2042 |
| 1 | 411 | 13 | Schäumig wie‘s sein soll. Ansonsten sehr üppig... | luigi.pietronello | 102 | 254 |
| 2 | 433 | 3 | Wawww | ademar.ju | 57 | 57 |
| 3 | 446 | 234 | Rich modern style that incorporates classic Ri... | akarter | 5886 | 147 |
| 4 | 520 | 1 | Good wine! | alessandra.fag | 14 | 20 |
| 5 | 547 | 514 | A fantastic Syrah, deep black color, deep anim... | scorewine | 16646 | 242 |
| 6 | 676 | 4 | Vin blanc très sucré, véritablement de l'or en... | alexandre.yi | 37 | 31 |
| 7 | 1848 | 0 | Very very Nice amarone. Fruitful and full with... | m58cb20b86fe892ae6f5543f9e9b2c8c | 0 | 0 |
| 8 | 5073 | 0 | Very very nice sauternes. | m58cb20b86fe892ae6f5543f9e9b2c8c | 0 | 0 |
Weird! It looks like m58cb20b86fe892ae6f5543f9e9b2c8c is a bot or experiment. Their profile shows some reviews with over 5,000 comments and zero likes. Here are comments for one of them:
User ademar.ju has reviewed 24 bottles which have single-digit likes, hundreds of comments each, all of which are written by Leonel de Paula. Leonel de Paula also comments on their own wine reviews with emojis, and tags other users.
Some users like kelley_do and scorewine appear to be real people with strong passions for wine.
There is a lot more I can say about these bots on Vivino that I will say in my next blog post. For now, I do not find any reviews from suspected bots with prices in them. This suggests that my work on price-sentiments is unlikely to suffer from bots generating fake reviews.
There are five numeric features for each user:
Let’s see how reviewers are connected to their communities.
q = """
SELECT user_followers_count, user_followings_count
FROM vivino
"""
tbl = pd.read_sql(q, sql_address)
plt.scatter(tbl['user_followers_count'], tbl['user_followings_count'], s= 1, alpha=0.5)
plt.title('Reviewer Connections')
plt.xlabel('Number of followers (celebrity status -->)')
plt.ylabel('Number of followings (watcher status -->)')
plt.xscale('log')
plt.show()
Wow! There’s a peculiar vase distribution. First, there’s some sort of uneven cap on the maximum number of people a reviewer can follow. The massive void on the left is striking and the reason is unclear just from this data. At the very least, it shows that you might be able to make connections just by following people. There is a lot of structure here that I will try to dig deeper in another post.
Let’s define a crude participation metric that equals the number of reviews authored plus ratings given. Then, plot this against the number of followers. (Yes, the results inherently ignore all Vivino users who do not write reviews).
q = """
SELECT user_followers_count, user_followings_count, user_reviews_count, user_ratings_count, user_seo_name
FROM vivino
"""
tbl = pd.read_sql(q, sql_address)
plt.scatter(tbl['user_followers_count'], tbl['user_reviews_count']+tbl['user_reviews_count'], s= 1, alpha=0.5)
plt.title('Writer Followers')
plt.xlabel('Number of followers')
plt.ylabel('Number of authored reviews + ratings')
plt.xscale('log')
plt.yscale('log')
plt.show()
There is a loose correlation between a user’s participation in reviews and ratings, and their follower count. My attention is drawn to the bottom where users have huge followings and participate very little. Let’s look at some of them:
q = """
SELECT user_followers_count as followers, user_followings_count as followings, user_ratings_count as ratings, user_reviews_count as reviews, user_seo_name as username
FROM vivino
WHERE user_reviews_count + user_ratings_count <= 4
AND user_followers_count > 200
ORDER BY user_followers_count
"""
tbl_bot = pd.read_sql(q, sql_address)
tbl_bot| followers | followings | ratings | reviews | username | |
|---|---|---|---|---|---|
| 0 | 210 | 409 | 1 | 1 | roberto.campane |
| 1 | 218 | 224 | 2 | 2 | m7748f668dbdc131b7330298a3f91cb3 |
| 2 | 228 | 63 | 2 | 1 | eduardo_silves |
| 3 | 267 | 0 | 1 | 1 | gabriel-moo |
| 4 | 287 | 257 | 2 | 2 | junior-ma |
| 5 | 292 | 517 | 1 | 1 | m778d4a83c4adc3c41786c1a8f45d28a |
| 6 | 300 | 293 | 2 | 2 | eva_bel |
| 7 | 528 | 85 | 1 | 1 | tino.ma |
Ah ha! The mysterious m7748f668dbdc131b7330298a3f91cb3 users.
I suspect the number of bots to be quite high. But, this is quite difficult to determine definitively without access to a user’s detailed logs. A potential gauge is number of characters in a username.
Woah! I wasn’t expecting a sizable number of users have the max length for usernames. Let’s take a look at some of the names.
| followers | followings | num_reviews | num_ratings | username | len_name | |
|---|---|---|---|---|---|---|
| 2074247 | 0 | 0 | 11 | 12 | edu.gaytan.eduardo101-gmail.com | 31 |
| 1177568 | 74 | 20 | 874 | 919 | university-of-bath.wine-society | 31 |
| 8325228 | 0 | 5 | 13 | 13 | jael.patricia-granados-kraulles | 31 |
| 8796594 | 7 | 7 | 261 | 262 | danton-cavalcanti.franco-junior | 31 |
| 3811532 | 1 | 0 | 36 | 38 | marc-antoine.mailloux-labrousse | 31 |
| 1067813 | 5 | 5 | 64 | 64 | b7941e714811716b34fec5f91046cae2 | 32 |
| 6142637 | 4 | 2 | 6 | 7 | d0a0d35afcfa00b39219856348c1cb12 | 32 |
| 10306389 | 52 | 28 | 1274 | 1303 | m276c28ed28bdf63ac70599392739db6 | 32 |
| 340985 | 6 | 8 | 19 | 23 | m4a610a084aeb6643b624f19501e5dbc | 32 |
| 8049959 | 393 | 105 | 312 | 312 | m2210f2e3d21d5f526d8119fae5b94bd | 32 |
| 1833623 | 0 | 1 | 5 | 5 | m9b16842b2ac0920a9bd932d634ae788 | 32 |
| 3694206 | 0 | 0 | 15 | 29 | m68f6cf0bd32342dd78045a1f291df1d | 32 |
| 208288 | 0 | 0 | 25 | 32 | c225723314b54f84b589ef58383ba9e8 | 32 |
| 7033437 | 2 | 0 | 86 | 86 | sylvain-est-bon-.il-faut-le-boir | 32 |
| 3228141 | 2 | 12 | 82 | 102 | m4fdc413aa3597af25f1fd807f596cca | 32 |
Ah. The 32-char names appear to be hashes. Some of these accounts appear legitimate. The backgrounds in their photos of wine appear to be the same environment for some users. It seems like a coincidence that the user with 5,000 comments on their reviews has the hash-like username.
Reviewers list the ‘notes’ of wine in their reviews. A nice surprise found in this data is a list of notes identified by Vivino. There are many, many notes.
Number of distinct notes: 456
My next blogs will be about wine reviews and…
Thank you for reaching the end!
@online{ro2023,
author = {Ro, Stephen},
title = {31 {Million} {Wine} {Reviews}},
date = {2023-07-06},
url = {https://royourboat.github.io/posts/2023-07-06-vivino-part1/},
langid = {en}
}