Trip.com Reviews Scraper | Extract Hotel Reviews, Ratings & Guest Feedback at Scale

Scrape Trip.com hotel 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, travel type, and room details --- ready for export to JSON, CSV, Excel, or API pipelines.

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🔥 Why use this Trip.com scraper?

• Extract real Trip.com reviews at scale
• Get ratings, review text, and translated content
• Capture travel type, room type, and usefulness metrics
• 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 Trip.com hotel URL
• Extract structured data including:
  • Review text (original + translated)
  • Ratings and rating scale
  • Travel type (e.g. Family, Couple)
  • Room type
  • Review usefulness
• Sorting options:
  • Most relevant
  • Most recent
  • Rating high → low
  • Rating low → high
• Incremental scraping via cutoff date
• Export to JSON, CSV, Excel, or integrate via API

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📥 Input

{
  "hotelUrl": "https://www.trip.com/hotels/shanghai-hotel-detail-346403/grand-hyatt-shanghai/",
  "maxReviews": 100,
  "sortBy": "mostRecent",
  "cutoffDate": "2024-01-01"
}

Input parameters

• hotelUrl (required): Trip.com hotel page URL\
• maxReviews: Maximum number of reviews to scrape (default: 1000)\
• sortBy:
  • mostRelevant
  • mostRecent
  • ratingHighToLow
  • ratingLowToHigh
• cutoffDate: Only return reviews newer than this date

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📤 Output

Each review is returned as structured data:

{
  "id": 1827940813,
  "usefulCount": 0,
  "language": "zh",
  "content": "在上海旅遊鎖定金茂君悦，選對根據地了！",
  "translatedContent": "I chose the Grand Hyatt Jin Mao for my trip to Shanghai.",
  "checkInDate": "2026-01-01 00:00:00",
  "createDate": "2026-01-27 19:11:11",
  "imageList": [
    "https://ak-d.tripcdn.com/images/example1.jpg"
  ],
  "videoList": [
    "https://video.c-ctrip.com/videos/example.mp4"
  ],
  "rating": 10.0,
  "ratingMax": 10,
  "commentLevel": "Outstanding",
  "roomTypeName": "Club Room Twin Bed",
  "travelType": 30,
  "travelTypeText": "Family",
  "translateFromRealReview": "Translation provided by AI",
  "feedbackContent": "Dear Valued Guest, thank you for choosing our hotel.",
  "feedbackTranslateContent": "Dear Valued Guest, thank you for choosing our hotel.",
  "feedbackLanguage": "zh",
  "feedbackCreateDate": "2026-01-28 00:00:00",
  "userNickName": "Guest User",
  "userCommentCount": 12
}

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📊 Use cases

📈 Market research & analytics

Analyze customer sentiment, satisfaction trends, and review patterns across hotels.

🏨 Competitor benchmarking

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

🤖 AI & data pipelines

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

📉 Reputation monitoring

Track how hotel perception changes over time.

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⚠️ Notes

• Trip.com often provides auto-translated reviews
• Some fields may be missing depending on the review
• Large hotels can have thousands of reviews
• Some reviews may include images, videos, hotel replies, and reviewer metadata
• Always comply with Trip.com Terms of Service

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💡 Pro tips

• Use mostRecent + cutoffDate for incremental scraping
• Run multiple jobs for large-scale datasets
• Normalize ratings (rating / ratingMax) for analytics
• Combine with other review sources for richer datasets (multi-platform analysis):
  • Agoda Reviews Scraper → https://apify.com/knagymate/fast-agoda-reviews-scraper
  • Kayak Reviews Scraper → https://apify.com/knagymate/apify-kayak-reviews-scraper
  • Priceline Reviews Scraper → https://apify.com/knagymate/priceline-reviews-scraper
  • Explore all scrapers → https://apify.com/knagymate