Airbnb Reviews Scraper | Extract Guest Reviews, Ratings & Host Responses at Scale

Scrape Airbnb listing reviews in seconds and transform them into structured datasets for data analysis, market research, sentiment analysis, and travel intelligence.

This actor extracts real guest reviews including ratings, translated content, host responses, reviewer details, and category rating breakdowns --- ready for export to JSON, CSV, Excel, or API pipelines.

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🔥 Why use this Airbnb scraper?

• Extract real Airbnb reviews at scale
• Get ratings, review text, and translated content
• Capture host responses, reviewer names, and category ratings
• Ideal for AI training, analytics, dashboards, and competitor research
• Built for fast, reliable, and scalable scraping

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🚀 Features

• Scrape reviews from any Airbnb listing URL
• Extract structured data including:
  • Review text (original + translated)
  • Star rating
  • Host responses (original + translated)
  • Reviewer and host (reviewee) names
  • Listing rating average and total rating count
  • Category rating breakdown (cleanliness, accuracy, check-in, communication, location, value)
• Sorting options:
  • Most relevant
  • Most recent
  • Highest rated
  • Lowest rated
• Incremental scraping via cutoff date
• Export to JSON, CSV, Excel, or integrate via API

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📥 Input

{
  "startUrl": "https://www.airbnb.hu/rooms/1453066826493838112",
  "maxReviews": 100,
  "sortBy": "mostRecent",
  "cutoffDate": "2024-01-01"
}

Input parameters

• startUrl (required): Airbnb listing page URL
• maxReviews: Maximum number of reviews to scrape (default: 100)
• sortBy:
  • mostRelevant
  • mostRecent
  • highestRated
  • lowestRated
• cutoffDate: Only return reviews newer than this date (when set, sorting automatically switches to mostRecent)

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📤 Output

Each review is returned as structured data:

{
  "id": "1491969879716769009",
  "createdAt": "2025-08-20 12:37:49+00:00",
  "comment": "Tolle Lage, schöner ruhiger Strand.",
  "localizedComment": "Great location, nice quiet beach.",
  "response": null,
  "localizedResponse": null,
  "language": "de",
  "rating": 4.0,
  "reviewerName": "Anita",
  "revieweeName": "Nativoo",
  "ratingAverage": 4.69,
  "ratingCount": 49,
  "ratingDetails": [
    {
      "rating": 4.7,
      "category": "CLEANLINESS"
    },
    {
      "rating": 4.8,
      "category": "ACCURACY"
    },
    {
      "rating": 4.6,
      "category": "CHECKIN"
    },
    {
      "rating": 4.7,
      "category": "COMMUNICATION"
    },
    {
      "rating": 4.6,
      "category": "LOCATION"
    },
    {
      "rating": 4.6,
      "category": "VALUE"
    }
  ]
}

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📊 Use cases

📈 Market research & analytics

Analyze guest sentiment, satisfaction trends, and review patterns across listings.

🏠 Competitor benchmarking

Compare listings based on ratings, feedback, and guest experience.

🤖 AI & data pipelines

Use structured reviews for: - NLP models - Sentiment analysis - Recommendation systems

📉 Reputation monitoring

Track how listing perception changes over time.

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⚠️ Notes

• Airbnb often provides auto-translated reviews
• Some fields may be missing depending on the review
• Popular listings can have hundreds of reviews
• Some reviews may include host responses and reviewer metadata
• Always comply with Airbnb Terms of Service

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💡 Pro tips

• Use mostRecent + cutoffDate for incremental scraping
• Run multiple jobs for large-scale datasets
• Use ratingDetails to analyze listing performance by category
• Combine with other review sources for richer datasets (multi-platform analysis):
  • Fast Agoda Reviews Scraper → https://apify.com/knagymate/fast-agoda-reviews-scraper
  • Trip.com Reviews Scraper → https://apify.com/knagymate/trip-com-reviews-scraper
  • Klook Reviews Scraper → https://apify.com/knagymate/klook-reviews-scraper
  • Explore all scrapers → https://apify.com/knagymate