Scrape Headout Reviews for Attraction Research

Headout reviews are useful when attraction research needs evidence beyond the headline score. The Headout Reviews Scraper turns public guest feedback into rows that can be filtered, grouped, and compared.

▶ Ready to run: Scrape Acropolis visitor reviews on Apify →

What the dataset captures

One run can return the original review, Headout's translated version, star rating, reviewer country, traveler persona, review date, customer photos, and stable tour identifiers. That supports destination research, attraction benchmarking, experience design, and travel-content planning without manually copying review pages.

Use a Headout activity URL or the numeric ID from its e-12045 suffix:

{
  "startUrls": [
    "https://www.headout.com/athens-acropolis-tickets/acropolis-parthenon-tickets-with-optional-audio-guide-e-12045/"
  ],
  "maxReviews": 250,
  "sortBy": "MOST_RECENT",
  "language": "EN"
}

The Actor uses Headout's JSON review endpoint directly, so it runs at 256 MB without browser startup or proxy overhead. Results are deduplicated before they reach the dataset, and pay-per-event billing applies only to rows written.

A practical research workflow

Start with 100–250 recent reviews. Group ratings by month and reviewer country, then inspect recurring phrases in low- and high-rating cohorts. Keep the original text for language-specific analysis and use translatedContent for a shared analysis layer. If an attraction has multiple ticket variants, retain tourId and tourGroupId so feedback is not accidentally blended.

For longitudinal research, schedule the Actor weekly with a cutoff date and store each raw snapshot. Compare review IDs between snapshots to identify new feedback, then alert only when rating mix or complaint themes move materially.

Common pitfalls

Do not treat a five-star review in one language as identical to its machine translation; retain both fields. Avoid comparing attractions with dramatically different review volumes using only averages. Finally, distinguish activity-level aggregate scores from the rating on each individual review.

Related guides: monitor Headout attraction ratings, analyze multilingual reviews, and collect guest photos.