How to Scrape Trustpilot Reviews by Company Domain (Python Guide)

Trustpilot is one of the most trusted review platforms globally, with 260 million reviews across 800,000 businesses. Getting that data programmatically for competitor analysis or brand monitoring is straightforward once you know the approach.

Try it live: Trustpilot Reviews Scraper, 10 Fields, Replies, No Login. Pay only for results delivered, no result no charge.

What is available

Every Trustpilot business profile contains:

• TrustScore (1-5) and star distribution
• Total review count and verified review count
• Per-review data: reviewer name, star rating, title, body, date, date of experience, and the business reply if one exists

All reviews are publicly visible without creating a Trustpilot account.

Using the Apify actor

import apify_client

client = apify_client.ApifyClient('YOUR_APIFY_TOKEN')

run_input = {
    "domain": "shopify.com",        # company domain
    "maxReviews": 500,
    "filterByStars": None,          # None = all, or [1, 2] for negative reviews
    "sortBy": "recency",
}

run = client.actor('themineworks/trustpilot-reviews').call(run_input=run_input)

for review in client.dataset(run['defaultDatasetId']).iterate_items():
    print(f"{'★' * review['stars']} — {review['title']}")
    print(f"  {review['body'][:100]}...")
    print(f"  — {review['reviewer_name']}, {review['date']}")
    if review.get('business_reply'):
        print(f"  Reply: {review['business_reply'][:80]}...")

Competitor sentiment analysis

Compare how customers describe you vs. competitors:

companies = ['your-company.com', 'competitor-a.com', 'competitor-b.com']
all_reviews = {}

for domain in companies:
    run = client.actor('themineworks/trustpilot-reviews').call(run_input={
        "domain": domain,
        "maxReviews": 200,
    })
    all_reviews[domain] = list(client.dataset(run['defaultDatasetId']).iterate_items())
    avg = sum(r['stars'] for r in all_reviews[domain]) / len(all_reviews[domain])
    print(f"{domain}: {avg:.2f} avg ({len(all_reviews[domain])} reviews)")

Negative review monitoring

Pull only 1 and 2-star reviews for complaint pattern analysis:

run_input = {
    "domain": "your-company.com",
    "filterByStars": [1, 2],
    "maxReviews": 500,
    "sortBy": "recency",
}

Tracking review velocity

Schedule weekly runs to track how review volume and score changes over time, a leading indicator of product or support quality shifts:

from datetime import datetime
import json

run_date = datetime.now().strftime('%Y-%m-%d')
results = list(client.dataset(run['defaultDatasetId']).iterate_items())

summary = {
    "date": run_date,
    "total_reviews": len(results),
    "avg_rating": sum(r['stars'] for r in results) / len(results),
    "trust_score": results[0].get('trust_score') if results else None,
}
print(summary)

Building a review training dataset

Trustpilot reviews with star ratings are a clean sentiment dataset for fine-tuning classifiers:

import csv

with open('trustpilot_training.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=['text', 'stars', 'sentiment'])
    writer.writeheader()
    for r in results:
        sentiment = 'positive' if r['stars'] >= 4 else 'negative' if r['stars'] <= 2 else 'neutral'
        writer.writerow({'text': r['body'], 'stars': r['stars'], 'sentiment': sentiment})

Pricing

Pay per review returned. Zero charge on empty searches.