Amazon review dataset: product review data

When crawlers meet Amazon reviews, have you stepped in any of these potholes?

Recently, a friend who does e-commerce came to me to complain, saying that he wanted to analyze the competitor's data, and as a result, he had just crawled 200 reviews, and his IP was blacked out by Amazon. This situation is too common, and many newbies are planted on the anti-crawl mechanism. Today, we will take the typical scenario of Amazon review data collection and talk about how to solve the problem elegantly with proxy IP.

Why is your crawler always blocked?

Amazon's anti-crawl system is much smarter than one might think. Let's take a real case: a user with a fixed IP request every 5 seconds, seems quite mild, right? As a result, the next day, the account was directly restricted access. Later, it was found that the system not only looks at the request frequency, but alsoDetecting Access Tracks. For example, consecutive visits to similar goods, and specific time periods of operation concentration may trigger wind control.

Proxy IPs in action

Here's where we have to bring out our savior - dynamic proxy IPs. A good IP pool should do three things:multiregional,Automatic frequency switching,Real User Behavior Simulation. For example, use ipipgo's residential proxy and change the end-user's IP in a different region for each request so that the system assumes that a real user is browsing.

import requests
from itertools import cycle

proxy_pool = cycle(ipipgo.get_proxy_list()) Get Dynamic IP Pools

for page in range(1, 50): proxy = next(proxy_pool): proxy = next(ipipgo.get_proxy_list)
    proxy = next(proxy_pool)
    try: response = requests.get(url)
        response = requests.get(url, proxies={"http": proxy, "https": proxy})
         Processing data logic...
    except Exception as e.
        print(f "IP {proxy} failed, automatically switching to the next one.")

Look for these hard indicators when choosing an agency service

Real User Case Studies

A cross-border e-commerce company needed to capture 100,000+ reviews for sentiment analysis. Initially used free proxies, as a result:

1. Triggers 20+ CAPTCHAs per day
2. Data duplication rate up to 35%
3. Acquisition cycle longer than 2 weeks

After switching to ipipgo's customized solution:

• Configure intelligent routing rules to automatically bypass high-risk areas
• Dynamically adjust IP switching policies in conjunction with request rates
• The collection was finally completed in 5 days, with valid data amounting to 98.71 TP3T

Frequently Asked Questions QA

Q: How many IPs do I need to prepare to be enough?
A: As a rule of thumb, it is recommended to prepare 50-80 quality IPs for every 1000 requests. in case of ipipgo users, theirIntelligent Dispatch SystemThe required quantity will be calculated automatically.

Q: What do I do when I encounter a CAPTCHA?
A: It is recommended to work with automated coding services, while paying attention to two points: 1) a single IP do not continuously trigger the verification 2) meet the verification to immediately switch the IP

Q: Is data scraping legal?
A: comply with robots agreement and website regulations, it is recommended to: 1) set a reasonable interval 2) not collect private information 3) for legitimate analysis purposes

Guide to avoiding pitfalls (focus here)

Three final hands-on suggestions:

1. Never use data center IPs, Amazon recognizes server room segments
2. Bring a different User-Agent for each request, but don't use one that's too cold
3. set upRandom Waiting TimeMimics real-life operating intervals

If you don't want to toss your own proxy pool maintenance, just use ipipgo'sAmazon Data Collection SolutionsThey have targeted parameter presets, more than their own ride to save money. Recently see the official website there are new users free trial activities, it is recommended that the first woolgathering to try the effect.