Trustpilot Reviews Scraper: New Reviews, Star Filter

Trustpilot Reviews: Deep Scrape with Star Filter

For each Trustpilot business slug, scrape reviews via the page's __NEXT_DATA__ JSON. One row per review. Optional star-filter pagination loops the business with stars=1..5 to multiply effective depth past Trustpilot's standard per-business window.

What you get

{
  "businessSlug":  "apify.com",
  "reviewId":      "abc123",
  "stars":         5,
  "title":         "Great service",
  "text":          "Used Apify for 3 months, saved us 40 hours weekly.",
  "language":      "en",
  "dateCreated":   "2026-05-12T08:30:00Z",
  "dateExperience":"2026-05-10",
  "reviewer": {
    "country":     "US",
    "numReviews":  3
  },
  "ownerReply": {
    "text":        "Thanks for the kind words, happy to hear it!",
    "date":        "2026-05-13T10:00:00Z"
  },
  "sentimentTags": ["positive_signal", "product_praise"]
}

Reviewer names and ids are not included; e-mail addresses and phone numbers inside review and reply text are replaced with [email removed] / [phone removed].

How to use

Input. A list of business slugs (apify.com) or full URLs (https://www.trustpilot.com/review/apify.com). URLs are reduced to slug.

Star-filter pagination. Trustpilot's default review feed exposes a limited number of pages per business (the actor reads up to 24 pages per feed). Turning on useStarFilterPagination runs the same business with stars=1, stars=2, …, stars=5 filters one after another, which reaches more reviews than the single feed. The total per business is capped by maxReviewsPerBusiness (default 200).

v0.2: what's new

sentimentTags. Every review is classified against an 8-tag lexicon based on Trustpilot's idiom. Tags are independent (a review can have several or none):

tag	what it signals
positive_signal	"excellent", "love it", "highly recommend", …
negative_signal	"terrible", "worst", "disappointed", …
scam_signal	"scam", "fraud", "ripped off", "stole my money", …
shipping_complaint	"shipping", "delivery", "arrived late", …
support_complaint	"no response", "ignored", "customer service", …
refund_complaint	"refund", "money back", "chargeback", …
price_complaint	"overpriced", "not worth", "hidden fee", …
product_praise	"quality", "well made", "works perfectly", …

Filter for scam_signal to surface fraud reports across a portfolio; filter for shipping_complaint over a 30-day window to track logistics regression.

Delta mode. Set deltaMode: true to charge only for new reviews since the last run. The actor persists a per-business seen-ID set in its KV store. First run emits everything; subsequent runs skip rows whose reviewId was already seen. Cap: 25 000 ids per business.

Use case, daily reputation monitoring: schedule a daily run with deltaMode: true, pay $0.0003 per new review only, get an empty dataset on quiet days.

Pricing

Pay per event. Price per 1,000 events:

Event	Base price	Bronze	Silver	Gold
Trustpilot review extracted	$0.30	$0.27	$0.24	$0.21

Bronze, Silver and Gold are the discounts Apify gives users of its paid plans. The Pricing tab always shows the current prices.

With delta mode on, you only pay for reviews you haven't seen before.

Run it through the API

Python, with the apify-client package:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("v0iddo/trustpilot-reviews-deep").call(run_input={"businesses": ["apify.com"], "maxReviewsPerBusiness": 20})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

JavaScript, with the apify-client package:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' });
const run = await client.actor('v0iddo/trustpilot-reviews-deep').call({"businesses": ["apify.com"], "maxReviewsPerBusiness": 20});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

One HTTP call that waits for the run and returns the rows (for runs that finish within 5 minutes):

curl -X POST "https://api.apify.com/v2/acts/v0iddo~trustpilot-reviews-deep/run-sync-get-dataset-items?token=<YOUR_APIFY_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"businesses": ["apify.com"], "maxReviewsPerBusiness": 20}'

Use it from an agent (MCP)

Claude, Cursor, VS Code and other MCP clients can call this actor as a tool through the Apify MCP server. Add it to the client's MCP configuration:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=v0iddo/trustpilot-reviews-deep",
      "headers": {
        "Authorization": "Bearer <YOUR_APIFY_TOKEN>"
      }
    }
  }
}

FAQ

Do I need an account, cookies or an API key?

No. The actor reads public data without logging in. You only need an Apify account to start it.

How many results can one run return?

Set Max reviews per business (up to 10,000) to the number you need. Large jobs can also be split into several runs or scheduled.

How am I charged?

Pay per event, at the prices on the Pricing tab for your Apify plan (the Pricing section of this page lists them). The platform usage of the run is included in those prices, and your spending limit for a run is always respected.

Why did my run return no results?

The status message and the log of the run say why: usually nothing matched the input, or the source did not answer for a moment. Check the input against the example above and run it again.

Can I run it on a schedule?

Yes. Save your input as a task and add a schedule in the Apify Console (daily, hourly or with a cron expression). Each run gets its own dataset, so you keep a history.

Which formats can I export?

JSON, CSV, Excel, XML, HTML table and RSS from the Output tab or the API. Integrations send the rows to Google Sheets, Slack, Make, Zapier, n8n or a webhook.

Is collecting this data allowed?

The actor collects data that the source publishes openly, without a login. Public data can still be personal data under laws such as the GDPR, and sources have their own terms: make sure your use of the data follows the rules that apply to you.

What happens when the source changes?

An automated test runs the actor every day on the example input and checks the rows, not only that the run finished. When a check fails, the actor is repaired and rebuilt; the build history on this page shows each update.

Something is missing or wrong. Where do I report it?

Open an issue on the Issues tab of this actor with the run link. Requests for a new field are welcome too.

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