Trustpilot Scraper — Reviews, Ratings & Sentiment Analysis

Extract Trustpilot reviews, company profiles, and TrustScores with built-in sentiment analysis. Type a company name or domain — no need to find the Trustpilot URL. 256MB, $0.50 per 1,000 reviews. 6x cheaper than the market leader.

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Why this Trustpilot Scraper?

Feature	This actor	getwally ($3/1K)	memo23 ($0.75/1K)	automation-lab ($0.58/1K)
Auto-resolve domains	Yes	No	No	Yes
Sentiment analysis	Built-in	No	No	No
Company reply included	Yes	?	Yes	Yes
Verified status	Yes	?	Yes	Yes
Star filter	Yes	?	Yes	Yes
Language filter	Yes	?	?	Yes
Memory	256MB	?	?	?
Success rate	99%+	99.6%	80%	99%
Price per 1,000	$0.50	$3.00	$0.75	$0.58

---

Quick start

Scrape by company domain (auto-resolved)

{
  "companies": ["pipedrive.com", "amazon.com", "nike.com"],
  "maxReviews": 100,
  "enableSentiment": true
}

Scrape by Trustpilot URL

{
  "urls": ["https://www.trustpilot.com/review/pipedrive.com"],
  "maxReviews": 200,
  "sortBy": "recency"
}

Filter negative reviews only

{
  "companies": ["example.com"],
  "maxReviews": 500,
  "starFilter": [1, 2],
  "enableSentiment": true
}

Filter by language

{
  "companies": ["amazon.fr"],
  "maxReviews": 100,
  "languageFilter": "fr"
}

---

Input

Parameter	Type	Default	Description
urls	array	—	Trustpilot company URLs
companies	array	—	Company names or domains (auto-resolved)
maxReviews	integer	100	Max reviews per company (1-5000)
sortBy	enum	recency	recency, rating_high, rating_low
starFilter	array	all	Filter by stars: [1, 2, 3, 4, 5]
languageFilter	string	all	ISO language code (en, fr, de, es...)
enableSentiment	boolean	false	Add AFINN-165 sentiment scoring
includeCompanyInfo	boolean	true	Include company profile as first result

---

What data do you get?

Company profile

{
  "type": "company",
  "name": "Pipedrive",
  "domain": "pipedrive.com",
  "trust_score": 4.4,
  "total_reviews": 3242,
  "stars": 4.5,
  "categories": ["CRM Software", "Sales Tools"],
  "website_url": "https://www.pipedrive.com/...",
  "profile_url": "https://www.trustpilot.com/review/pipedrive.com"
}

Review (35+ fields)

{
  "type": "review",
  "id": "abc123",
  "title": "Great CRM but support needs work",
  "text": "Full review text here...",
  "rating": 4,
  "date": "2026-03-27T23:17:29.000Z",
  "experience_date": "2026-03-26T00:00:00.000Z",
  "updated_date": "2026-03-31T16:06:06.000Z",
  "author": "Claire H.",
  "author_country": "CA",
  "author_reviews_count": 4,
  "verified": true,
  "source": "Organic",
  "likes": 3,
  "language": "en",
  "company_reply": "Thank you for your feedback...",
  "company_reply_date": "2026-03-31T15:58:30.000Z",
  "company_name": "Pipedrive",
  "company_domain": "pipedrive.com",
  "company_trust_score": 4.4,
  "sentiment_score": -1.5,
  "sentiment_label": "negative"
}

---

Sentiment analysis

When enableSentiment: true, each review gets:

• sentiment_score — AFINN-165 average per word (-5 to +5)
• sentiment_label — positive, negative, or neutral

Built-in, no external API. Handles negation ("not good" = negative).

---

How much does it cost?

$0.0005 per result = $0.50 per 1,000 reviews.

Scenario	Results	Cost	Time
1 company, 20 reviews	21	$0.01	~1s
1 company, 100 reviews	101	$0.05	~3s
5 companies, 200 each	1,005	$0.50	~30s
10 companies, 500 each	5,010	$2.50	~3min

256MB memory = negligible compute cost.

---

Use cases

• Competitor analysis — compare TrustScores and review sentiment across competitors
• Brand monitoring — track new reviews and sentiment trends
• Market research — analyze customer pain points in your industry
• Sales intelligence — identify companies with poor reviews (potential leads for better alternatives)
• Content research — find real customer quotes for marketing copy
• Compliance — monitor review patterns for fake review detection

---

API integration

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("dltik/trustpilot-scraper").call(run_input={
    "companies": ["pipedrive.com", "hubspot.com"],
    "maxReviews": 100,
    "starFilter": [1, 2],
    "enableSentiment": True,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item["type"] == "review" and item.get("sentiment_label") == "negative":
        print(f"[{item['rating']}*] {item['title']}")

curl

curl -X POST "https://api.apify.com/v2/acts/dltik~trustpilot-scraper/runs" \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "companies": ["pipedrive.com"],
    "maxReviews": 50,
    "enableSentiment": true
  }'

---

Technical details

• HTTP-only — parses __NEXT_DATA__ JSON from Trustpilot HTML pages, no browser
• 256MB memory — lightest Trustpilot scraper on Apify
• Auto-resolve — type nike.com and it finds the Trustpilot page automatically
• Pagination — 20 reviews per page, automatic multi-page fetching
• Rate limiting — 0.5s between requests to respect Trustpilot
• No proxy needed — Trustpilot doesn't block datacenter IPs on HTML pages
• Batch processing — scrape multiple companies in one run

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FAQ

Do I need a proxy? No. Trustpilot serves HTML pages to all IPs. No proxy required.

How many reviews can I get? Up to 5,000 per company. Trustpilot shows all public reviews via pagination.

What's auto-resolve? Type pipedrive.com or amazon in the companies field — the actor automatically finds the correct Trustpilot review page without you needing to look up the URL.

Is sentiment analysis accurate? AFINN-165 captures overall sentiment direction well for English reviews. For multilingual deep analysis, export and use a specialized NLP tool.

Can I filter only negative reviews? Yes. Set starFilter: [1, 2] to get only 1-star and 2-star reviews.

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Connect with Make, Zapier & n8n

This actor integrates with any automation platform via the Apify API.

Make (Integromat)

1. Add an Apify module in your Make scenario
2. Select Run Actor and choose this actor
3. Configure the input (paste your JSON)
4. Add a Get Dataset Items module to retrieve results
5. Connect to Google Sheets, HubSpot, Slack, or any other app

Zapier

1. Use the Apify integration on Zapier
2. Set trigger: Actor Run Finished
3. Action: Get Dataset Items
4. Send results to your CRM, email tool, or spreadsheet

n8n

1. Add an HTTP Request node to call the Apify API
2. POST to https://api.apify.com/v2/acts/dltik~trustpilot-scraper/runs
3. Wait for completion, then fetch dataset items
4. Route results to any n8n node

Webhooks

Set up a webhook to get notified when a run finishes:

run = client.actor("dltik/trustpilot-scraper").call(
    run_input={...},
    webhooks=[{
        "eventTypes": ["ACTOR.RUN.SUCCEEDED"],
        "requestUrl": "https://your-webhook-url.com"
    }]
)

---

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