IPIPGO ip proxy Yelp Data Capture: Merchant Review Capture Solution

Yelp Data Capture: Merchant Review Capture Solution

Why Yelp reviews in a real scenario? A friend of mine who owns a restaurant recently came to me and said he wanted to see customer feedback on his restaurant. These days, it's not enough to just know how to cook, you need to know the real reviews to improve your service, and with thousands of reviews on Yelp, it can be exhausting to manually transcribe them, so you need to...

Yelp Data Capture: Merchant Review Capture Solution

Why Yelp reviews in a real scenario?

Recently, my friend who owns a restaurant approached me and said that he wanted to see the customer feedback of the store. Nowadays, it's not enough to just know how to cook, you have to find out the real user reviews in order to improve the service. thousands of reviews on Yelp, manually transcribing them can be exhausting, it's necessary to use automation to collect them.

But directly open the crawler is easy to be blocked IP, especially when continuous request. Last year, there was a case where a chain brand used a single IP to capture data, and as a result triggered a wind control that led to the entire company's network being blacked out for three days, resulting in heavy losses.

How do proxy IPs break this?

Here's one.Key Perceptions: The website blocking mechanism looks at two main indicators - frequency of visits and IP trajectory. As if you go to the bank to withdraw money, the counter every day to see hundreds of people, but if the same person within ten minutes to repeatedly come to do business, the security guard must pay attention.

Using ipipgo's proxy pooling service is like changing your clothes + disguise every time you go into the bank. This is done in three steps:


import requests
from itertools import cycle

 List of proxies from ipipgo
proxies = [
    "http://user:pass@gateway.ipipgo:9020",
    "http://user:pass@gateway.ipipgo:9021".
     ... Other nodes
]
proxy_pool = cycle(proxies)

for page in range(1, 101): current_proxy = next(proxies)
    current_proxy = next(proxy_pool)
    current_proxy = next(proxy_pool)
        resp = requests.get(
            f "https://www.yelp.com/biz/xxx/review_feed?start={page20}",
            proxies={"http": current_proxy}, timeout=8, timeout=8, current_proxy = next(proxy_pool)
            timeout=8
        )
         Parsing data logic...
    except Exception as e.
        print(f "Rollover with {current_proxy}: {str(e)}")

A practical guide to avoiding the pit

Don't think that just because you've hooked up with an agent that everything will be fine, here are a couple oflesson learned through blood and tears::

1. Don't use free proxies (not to mention slow speeds and the possibility of being attacked by a man-in-the-middle)
2. Randomly change the User-Agent for each request, don't use Python's default one.
3. Control the rhythm of access, it is recommended that each page interval of 3-8 seconds random dormant
4. Pause immediately when encountering CAPTCHA, change to a new IP address and try again.

Recommended for ipipgoLong-lasting static residential IPThis kind of IP with real home broadband attributes is more difficult to be recognized than server room IPs. The actual test with his U.S. residential nodes, continuous collection of 200 pages before triggering the verification, ordinary room IP usually 30 pages on the hang.

Frequently Asked Questions QA

Q: Is it legal to harvest Yelp reviews?
A: Depending on the use of the data, it is recommended that only publicly visible content be captured and that it not be used for commercial competition. Best to consult legal counsel

Q: How do I choose an agent package for ipipgo?
A: Use pay-as-you-go package for small projects and monthly package for long-term needs. New subscribers should remember to get the 3G traffic trial package

Q: Where is the appropriate place to store the collected data?
A: It is recommended to use CSV format storage, fields contain the content of the review, rating, date. Do not store directly in the database, it is easy to leave traces of the operation

Advanced Tips: Distributed Acquisition

When it is necessary to collect data from multiple cities, you can use ipipgo'sCity-level positioningFunction. For example, to catch restaurant reviews in Los Angeles and New York, specifying the exit IPs of these two cities separately can reduce the probability of being backcrawled.

Here's a sample configuration sheet:

target city Area of Representation concurrency
Los Angeles, California US-LAX 3 threads
NY US-NYC 3 threads

A final reminder: data collection is the art of balance, both efficiency and stealth. Choose the right tool is only the first step, continue to adjust the strategy in order to long-term stable operation. With ipipgo's customer support services, encounter technical problems can be directly to their engineers to ready-made programs, more than their own toss to save effort.

This article was originally published or organized by ipipgo.https://www.ipipgo.com/en-us/ipdaili/32925.html

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