CODEWITHSUNDEEP
pythonhtmlweb-scrapingbeautifulsoup
doomdaam
doomdaam

Reputation: 783

Scraping Amazon reviews, cannot exclude paid reviews

I'm trying to scrape the number of stars each reviewer gives a product. I noticed some reviewers are "Vine Voices" or paid reviewers. They rarely give 4, mostly 5 stars. Therefore, I'd like to exclude them.

The way I do this is by tagging them "Paid" or "Not-paid" if a review is marked with "a-color-success a-text-bold" tag.

I can't seem to append any "Paid" tags into the vine variable. How come?

Only those reviews who are written by a Vine Voice, has the tag, those who don't does not have the tag in "paid". 

import requests
from bs4 import BeautifulSoup
import pandas as pd
import time

headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36'}

rating_list = [] 
date_list = []
vine = []

for num in range(1,12):
    url = "https://www.amazon.com/Jabra-Wireless-Noise-Canceling-Headphones-Built/product-reviews/B07RS8B5HV/ref=cm_cr_arp_d_paging_btm_next_2?ie=UTF8&reviewerType=all_reviews&pageNumber={}&sortBy=recent".format(num)

    r = requests.get(url, headers = headers)

    soup = BeautifulSoup(r.content, 'lxml')

    for ratings in soup.find_all("div", attrs={"data-hook": "review"}):     
        submission_date = ratings.find("span", {'data-hook':'review-date'}).text
        rating = ratings.find('i', attrs={"data-hook": "review-star-rating"}).text
        paid = ratings.find("span", attrs={"class": "a-color-success a-text-bold"})

        if paid in ratings:
             vine.append("Paid")
        else:
            vine.append("Not-paid")

            date_list.append(submission_date)
            rating_list.append(rating)

            data = {'Rating':rating_list, 'Date':date_list, "Paid":vine}
        time.sleep(2)

df = pd.DataFrame(data)
df["Date"] = pd.to_datetime(df["Date"])
df = df.sort_values(by="Date", ascending=False)
print(df)

This is what I get so far. Review 2 and 3 are Vine Voice, but they are getting tagged as Not-Paid, but should be Paid.

0    5.0 out of 5 stars 2019-09-18  Not-paid
1    4.0 out of 5 stars 2019-09-13  Not-paid
2    5.0 out of 5 stars 2019-09-12  Not-paid
3    5.0 out of 5 stars 2019-09-11  Not-paid
4    5.0 out of 5 stars 2019-09-10  Not-paid
...

Upvotes: 2

Views: 244

Answers (2)

QHarr
QHarr

Reputation: 84465

I think a better way (with bs4 4.7.1+) is to use :has and :not to do the exclusion up front. You then don't need the exclude field/flag. In the following I print out the reviewer name as a visual check (you will see that the paid reviewers names don't appear). I also adjust your loop to work properly and use Session for efficiency. I also use shorter more robust selectors.

css selectors are faster than find so I would probably change find lines to:

submission_date = review.select_one('[data-hook=review-date]').text
rating = review.select_one('[data-hook=review-star-rating]').text

Py

import requests
from bs4 import BeautifulSoup
import pandas as pd

headers = {'User-Agent': 'Mozilla/5.0'}
rating_list = [] 
date_list = []

with requests.Session() as s:   
    for num in range(1,12):
        url = "https://www.amazon.com/Jabra-Wireless-Noise-Canceling-Headphones-Built/product-reviews/B07RS8B5HV/ref=cm_cr_arp_d_paging_btm_next_2?ie=UTF8&reviewerType=all_reviews&pageNumber={}&sortBy=recent".format(num)
        r = s.get(url, headers = headers)
        soup = BeautifulSoup(r.content, 'lxml')

        for review in soup.select('.review:not(:has(.a-color-success))'):   
            submission_date = review.select_one('[data-hook=review-date]').text
            rating = review.select_one('[data-hook=review-star-rating]').text
            date_list.append(submission_date)
            rating_list.append(rating)
            print(review.select_one('.a-profile-name').text) #check 
    data = {'Rating':rating_list, 'Date':date_list}

df = pd.DataFrame(data)
df["Date"] = pd.to_datetime(df["Date"])
df = df.sort_values(by="Date", ascending=False)
print(df)

Upvotes: 2

KunduK
KunduK

Reputation: 33384

You comparing element with element and that is why its getting to else condition always. I have made changes and compared text with text and it is working fine.Check the below code.

import requests
from bs4 import BeautifulSoup
import pandas as pd
import time

headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36'}

rating_list = []
date_list = []
vine = []

for num in range(1,12):
    url = "https://www.amazon.com/Jabra-Wireless-Noise-Canceling-Headphones-Built/product-reviews/B07RS8B5HV/ref=cm_cr_arp_d_paging_btm_next_2?ie=UTF8&reviewerType=all_reviews&pageNumber={}&sortBy=recent".format(num)

    r = requests.get(url, headers = headers)

    soup = BeautifulSoup(r.content, 'lxml')

    for ratings in soup.find_all("div", attrs={"data-hook": "review"}):
        submission_date = ratings.find("span", {'data-hook':'review-date'}).text
        rating = ratings.find('i', attrs={"data-hook": "review-star-rating"}).text
        paid = ratings.find("span", attrs={"class": "a-color-success a-text-bold"})
        if paid:

         if paid.text in ratings.text:
             vine.append("Paid")
             date_list.append(submission_date)
             rating_list.append(rating)

             data = {'Rating': rating_list, 'Date': date_list, "Paid": vine}
        else:
            vine.append("Not-paid")

            date_list.append(submission_date)
            rating_list.append(rating)

            data = {'Rating':rating_list, 'Date':date_list, "Paid":vine}
        time.sleep(2)

df = pd.DataFrame(data)
df["Date"] = pd.to_datetime(df["Date"])
df = df.sort_values(by="Date", ascending=False)
print(df)

Output:

          Date      Paid              Rating
0   2019-09-18  Not-paid  5.0 out of 5 stars
1   2019-09-13  Not-paid  4.0 out of 5 stars
2   2019-09-12      Paid  5.0 out of 5 stars
3   2019-09-11      Paid  5.0 out of 5 stars
4   2019-09-10  Not-paid  5.0 out of 5 stars
5   2019-09-10  Not-paid  2.0 out of 5 stars
6   2019-09-10      Paid  5.0 out of 5 stars
7   2019-09-09      Paid  5.0 out of 5 stars
8   2019-09-09  Not-paid  2.0 out of 5 stars
9   2019-09-08      Paid  5.0 out of 5 stars
10  2019-09-05      Paid  5.0 out of 5 stars
11  2019-09-01  Not-paid  2.0 out of 5 stars
12  2019-08-31      Paid  5.0 out of 5 stars
13  2019-08-25      Paid  5.0 out of 5 stars
14  2019-08-24  Not-paid  4.0 out of 5 stars
15  2019-08-22  Not-paid  5.0 out of 5 stars
16  2019-08-21      Paid  5.0 out of 5 stars
17  2019-08-20  Not-paid  5.0 out of 5 stars
18  2019-08-20      Paid  5.0 out of 5 stars
19  2019-08-18      Paid  5.0 out of 5 stars
20  2019-08-17  Not-paid  5.0 out of 5 stars
21  2019-08-17  Not-paid  5.0 out of 5 stars
22  2019-08-14  Not-paid  4.0 out of 5 stars
23  2019-08-12      Paid  5.0 out of 5 stars
24  2019-08-05      Paid  5.0 out of 5 stars
25  2019-08-05      Paid  4.0 out of 5 stars
26  2019-08-04      Paid  5.0 out of 5 stars
27  2019-08-04      Paid  4.0 out of 5 stars
29  2019-08-03      Paid  5.0 out of 5 stars
28  2019-08-03      Paid  4.0 out of 5 stars
..         ...       ...                 ...
80  2019-07-08      Paid  5.0 out of 5 stars
81  2019-07-08      Paid  5.0 out of 5 stars
82  2019-07-08      Paid  5.0 out of 5 stars
85  2019-07-07      Paid  5.0 out of 5 stars
83  2019-07-07      Paid  5.0 out of 5 stars
84  2019-07-07      Paid  5.0 out of 5 stars
87  2019-07-06      Paid  5.0 out of 5 stars
86  2019-07-06      Paid  4.0 out of 5 stars
88  2019-07-05  Not-paid  4.0 out of 5 stars
89  2019-07-05      Paid  5.0 out of 5 stars
90  2019-07-05      Paid  5.0 out of 5 stars
91  2019-07-05      Paid  5.0 out of 5 stars
92  2019-07-04      Paid  5.0 out of 5 stars
93  2019-07-04      Paid  4.0 out of 5 stars
94  2019-07-04      Paid  5.0 out of 5 stars
95  2019-07-04      Paid  5.0 out of 5 stars
96  2019-07-04      Paid  5.0 out of 5 stars
98  2019-07-03  Not-paid  3.0 out of 5 stars
97  2019-07-03      Paid  5.0 out of 5 stars
99  2019-07-01      Paid  5.0 out of 5 stars
100 2019-07-01      Paid  3.0 out of 5 stars
101 2019-07-01      Paid  5.0 out of 5 stars
102 2019-06-30      Paid  5.0 out of 5 stars
103 2019-06-29      Paid  5.0 out of 5 stars
104 2019-06-29      Paid  5.0 out of 5 stars
105 2019-06-28  Not-paid  1.0 out of 5 stars
106 2019-06-27      Paid  4.0 out of 5 stars
107 2019-06-27      Paid  5.0 out of 5 stars
108 2019-06-26      Paid  5.0 out of 5 stars
109 2019-06-26      Paid  5.0 out of 5 stars

[110 rows x 3 columns]

Upvotes: 2

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