Review Cleaner & Deduplicator — Fakes, Duplicates, Score

Clean review exports from any source into one schema: rating, title, text, author, date and ID, with duplicates removed and fake-looking rows scored. Compare two exports to isolate genuinely new reviews. Bring your own data; no scraping. For analysts and reputation teams.

In short: what you give, what you get, what it costs

You give	Your own review exports (from any site), one or two of them
We do	Put them into one format, remove duplicates, score fake-looking reviews, and with two exports keep only the new ones
You get	One row per review: rating, title, text, author, date, ID, topic, severity and the signals behind the fake score
It costs	$0.02 per review row delivered + $0.001 per run. The first 10 results of every run are free. No scraping, so no supplier fee
It does not	Collect reviews from websites: you bring the data

A real result row is shown further down this page.

Free tier — 10 results free on every run

The first 10 results of every run are free, on every run you ever make — not a one-off trial. Run it on your own subjects and see the real output before you are billed for a single result.

One guard: the free allowance never covers more than half of a run, so a run returning 6 results gets 3 free. That keeps the offer sustainable.

What it does

Run it once and get the full, current data as clean rows you can export to JSON, CSV or Excel, or pull through the API. Need only fresh data? Turn on monitorEnabled and schedule it: every later run returns only what is new or changed since the previous one, so you never re-download or re-pay for the same rows.

Why this scraper

1. Try before you pay. The first 10 results of every run are free, on every run.
2. Pay only for data delivered. Failed sources and failed inputs are never billed.
3. Hard cost cap. Set maxTotalChargeUsd and a run can never cost more than you allow.
4. Only-new mode. With monitorEnabled on a schedule, you get just the new or changed rows, plus a spike signal when volume suddenly jumps.
5. Clean, normalized rows with the same field names every run, plus a RUN_SUMMARY.json — ready for spreadsheets, databases, n8n/Make/Zapier and AI agents (MCP).

Example input

{
  "maxTotalChargeUsd": 1.0
}

This returns the full current data right away. To receive only what is new on a schedule, add "monitorEnabled": true: the first monitored run stores a free baseline, later runs return only new or changed rows. Pin a monitorId to keep separate targets from sharing state.

Output

Each row is one review. A real row from a run on Apify (long texts are cut here and people's names hidden; your dataset has them in full):

A real row from this Actor's latest run on Apify (2026-10-23; long texts are cut and people's names hidden here, your dataset has them in full):

{
  "reviewId": "r1",
  "rating": 2,
  "text": "Package arrived late and damaged.",
  "author": "(hidden here)",
  "record_id": "902c051b3cd8bb0e8161598e",
  "change_type": "new",
  "actionable": true,
  "signals": [
    "response_needed"
  ],
  "topic": "shipping",
  "severity": "moderate",
  "why_it_matters": "New 2-star negative review needs attention. Worth a quick reply."
}

Field	What it holds
rating	stars
text	review text
topic	what the review is about
severity	how serious a complaint is
signals	what the review reveals
why_it_matters	one-line reading for the business

Each row also keeps the other fields the source returns, so you get everything above and more. In the Output tab, the Overview table shows these columns, readable without any code; export them as CSV, Excel, JSON or HTML.

How you're charged

• Review alert — billed once per new review (the only charge).
• Bring-your-own dataset — you pass the records in, so there is no upstream fetch and no source-check fee.
• A run with no new reviews costs $0.

Related tools

• Need the reviews collected first, not just cleaned up? → Google Maps Reviews Scraper - Rating, Text, Reviewer

How to use it in 3 steps

1. Click Try for free and fill in Current reviews (currentReviews).
2. Optional: adjust the other fields; every one has a description and a sensible default.
3. Click Start and download the results as JSON, CSV or Excel, or read them from the API.

Use cases

• Analysts
• Reputation teams

Use it from code (API)

Every Apify Actor is an API. Start a run and read the results with one call.

Python

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("om_kh/reviews-cleaner-deduplicator").call(run_input={"currentReviews": [{"reviewId": "r1", "rating": 2, "text": "Package arrived late and damaged.", "author": "Sam"}]})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

JavaScript

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' });
const run = await client.actor('om_kh/reviews-cleaner-deduplicator').call({
    "currentReviews": [
        {
            "reviewId": "r1",
            "rating": 2,
            "text": "Package arrived late and damaged.",
            "author": "Sam"
        }
    ]
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

One HTTP call (cURL) — runs the Actor and returns the dataset in the same response:

curl -X POST "https://api.apify.com/v2/acts/om_kh~reviews-cleaner-deduplicator/run-sync-get-dataset-items?token=<YOUR_APIFY_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"currentReviews": [{"reviewId": "r1", "rating": 2, "text": "Package arrived late and damaged.", "author": "Sam"}]}'

Integrations: n8n, Make, Zapier, Google Sheets

• n8n — the official Apify node, operation Run Actor and get dataset, Actor om_kh/reviews-cleaner-deduplicator.
• Make — Apify › Run an Actor, then Apify › Get Dataset Items.
• Zapier — Apify › Run Actor; map the dataset items into your next step.
• Google Sheets, webhooks, Slack — add an integration from the Integrations tab of this Actor.
• Schedules — run it every hour or day from Schedules in Apify Console.

Use it from an AI agent (MCP and x402)

Connect Claude Desktop, Cursor, VS Code or any MCP client to Apify's MCP server at https://mcp.apify.com and add this Actor, om_kh/reviews-cleaner-deduplicator, as a tool — the agent passes the same JSON as the run input and gets the dataset back.

AI agents that pay with x402 can run it too, without an Apify account: the Actor charges only per result delivered, so an agent pays exactly for what it gets.

FAQ

Is it legal to use it? This Actor visits no website: it only cleans the reviews you send in currentReviews. You are responsible for having the right to use those reviews; if they contain personal data (author names), check your local law (GDPR, CCPA), and ask a lawyer if unsure.

Do I need an account, cookies or an API key? No. You only need an Apify account to run it.

What data do I get? Structured records. Download it as JSON, CSV, Excel, HTML or XML, or read it from the API.

Am I charged when something fails? No. An input that fails is listed with its reason in the run summary and is never charged.

Can I run it on a schedule? Yes. Create a schedule in Apify Console (hourly, daily, weekly) and connect webhooks, Google Sheets, Slack, n8n, Make or Zapier.

Can I use it from Python, JavaScript or an AI agent? Yes: see the code samples above, or connect AI agents through MCP with the reviews_delta tool.

Your feedback

Did this Actor save you time? A short review on the Reviews tab helps other people find it, and every review is read. Something missing or broken? Open an issue on the Issues tab: it is answered and fixed fast.

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