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Review Dataset Auditor

Pricing

from $2.00 / 1,000 results

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Review Dataset Auditor

Review Dataset Auditor

Audit review Datasets for completeness, malformed fields, duplicate IDs, and duplicate content.

Pricing

from $2.00 / 1,000 results

Rating

0.0

(0)

Developer

Agent Workflow Lab

Agent Workflow Lab

Maintained by Community

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0

Bookmarked

2

Total users

1

Monthly active users

23 days ago

Last modified

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Audit heterogeneous review records before analytics, triage, or response workflows. The Actor produces one deterministic report covering usable review text, rating/date/author completeness, duplicate IDs, and exact normalized-text duplicates across common Google Maps, Amazon, App Store, and generic review fields.

No external API key or LLM is required. The Actor makes no external model or third-party API calls.

Input

Use the Source Dataset READ-only resource picker, pass reviews inline, or run {} for a clearly labeled embedded sample. Input precedence is: presence of reviews (including []), then nonblank datasetId, then sample.

{
"datasetLabel": "May review export",
"reviews": [
{ "id": "r-1", "text": "Exports fail every afternoon.", "rating": 2, "date": "2026-05-01" },
{ "id": "r-1", "text": "Exports fail every afternoon.", "rating": 2, "date": "2026-05-01" }
],
"maxDuplicateGroups": 25
}

Output and pricing shape

Every run writes exactly one review_dataset_audit_report Dataset item and the same report to OUTPUT. One aggregate audit equals one Dataset result. Synthetic Dataset-item charging is automatic; the code contains no manual charging.

The quality score is a bounded deterministic readiness indicator, not a fraud or authenticity score. Exact duplicate content is matched only after lowercasing and whitespace normalization; semantic near-duplicates are not inferred.

Limits and privacy

  • At most 5,000 source records and 100 returned duplicate groups.
  • Review text is inspected through the tested Review Intelligence normalization boundary; no raw review body is copied into duplicate-group output.
  • Source Dataset access is READ-only and never writes to or deletes the source.
  • User data is written only to the run's default Dataset and OUTPUT; Apify retention settings apply.