Scrape reviews by product keyword

To scrape reviews by product keyword, you typically need to perform web scraping on e-commerce sites (like Amazon, Walmart, BestBuy, etc.), review aggregators, or forums where users leave product feedback. Here’s a high-level breakdown of how you can do this:

1. Define the Product Keyword

Start by identifying your keyword(s). Example: "wireless earbuds", "air fryer", "laptop stand".

2. Identify Target Websites

Choose where you’ll scrape reviews from. Common targets include:

• Amazon (requires careful handling due to anti-bot mechanisms)

• Walmart

• BestBuy

• Target

• eBay

• Google Shopping

• Reddit or specialized forums

• Review aggregation platforms (like Trustpilot, ConsumerAffairs)

3. Use a Scraping Tool or Script

Option A: Use Python with BeautifulSoup/Selenium

Example using Google Shopping:

python
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import requests
from bs4 import BeautifulSoup
from urllib.parse import quote

def scrape_google_shopping_reviews(keyword):
    search_url = f"https://www.google.com/search?q={quote(keyword)}+site:shopping.google.com"
    headers = {"User-Agent": "Mozilla/5.0"}

    response = requests.get(search_url, headers=headers)
    soup = BeautifulSoup(response.text, 'html.parser')

    results = soup.find_all('div', class_='BVG0Nb')  # Change as needed depending on site structure
    for result in results:
        title = result.find('h3')
        snippet = result.find('span', class_='aCOpRe')
        if title and snippet:
            print("Title:", title.text)
            print("Review Snippet:", snippet.text)
            print("---")

scrape_google_shopping_reviews("wireless earbuds")

Option B: Use a Web Scraping API (e.g., SerpAPI, ScraperAPI)

These help avoid IP bans and handle captchas.

Example using SerpAPI (free tier available):

python
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from serpapi import GoogleSearch

params = {
    "engine": "google",
    "q": "wireless earbuds site:amazon.com",
    "api_key": "YOUR_SERPAPI_KEY"
}

search = GoogleSearch(params)
results = search.get_dict()

for res in results.get("organic_results", []):
    print(res.get("title"))
    print(res.get("snippet"))

4. Store and Clean the Data

Use Pandas to clean and save:

python
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import pandas as pd

# Sample structure
df = pd.DataFrame({
    'Title': titles,
    'Review': snippets,
    'Source': urls
})

df.to_csv("product_reviews.csv", index=False)

5. (Optional) Use NLP to Analyze Reviews

For example, extract sentiment:

python
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from textblob import TextBlob

df['Sentiment'] = df['Review'].apply(lambda x: TextBlob(x).sentiment.polarity)

Important Notes

• Scraping Amazon or similar e-commerce sites directly may violate their terms of service.

• Use APIs or public sources when possible.

• Rotate IPs and user agents when doing large-scale scraping.

• Consider using a headless browser like Selenium or Playwright when JS rendering is required.

If you want a ready-to-use script for a specific platform and keyword, let me know which platform and keyword(s) you’re targeting.