Sletat Review Scraper

Pricing

Rating

Developer

Actor stats

Categories

Share

Overview

Sletat.ru hotel review scraper for Apify

Collect Sletat.ru hotel reviews with detailed rating breakdowns, filters, and hotel-level overview statistics. Output is structured JSON ready for sentiment analysis, competitor tracking, and reputation management.

What’s Collected?

• Review text: positive/negative parts, title
• Ratings: room, cleanliness, location, service, price/performance
• Metadata: author name, visit date, trip type, user avatar, photos
• Management responses: response text and date
• Hotel overview: average rating, seasonal distribution, good/medium/bad counts, additional rating averages/counts

Highlights

• Advanced filters for Sletat reviews: photos, detailed text, detailed ratings, sentiment (positive/negative)
• Flexible limits: reviews per hotel (0 = All )
• Structured output: clean JSON datasets for Apify, BI tools, and data pipelines — aligned with common Sletat Hotel Reviews API fields

Quick Start

1. Add one or more Sletat.ru hotel page URLs to startUrls. Example: https://sletat.ru/uae/dubai_marina/hilton_dubai_jumeirah_resort/
2. Select optional filters: onlyWithPhotos, onlyWithDetailedText, onlyWithDetailedRatings, feedbackType.
3. Set maxReviewsPerHotel (0 = All Reviews).
4. Start the actor (Sletat scraper). When the run finishes, download the Dataset from the Output tab or fetch it via API.

Input

Input Example

{
"startUrls":[{"url":"https://sletat.ru/uae/dubai_marina/hilton_dubai_jumeirah_resort/"}],
"maxReviewsPerHotel":500,
"onlyWithPhotos":false,
"onlyWithDetailedText":false,
"onlyWithDetailedRatings":false,
"feedbackType":"all"
}

Output

Each review is stored as a single dataset item with hotel information and a hotel-level overview object, mirroring common Sletat Hotel Reviews API aggregates.

Sample Item

{
"hotelId":12345,
"hotelName":"Hilton Dubai Jumeirah",
"hotelUrl":"https://sletat.ru/uae/dubai_marina/hilton_dubai_jumeirah_resort/",
"reporterName":"Anna K.",
"rating":9,
"title":"Excellent family vacation!",
"positiveFeedback":"Great animation team, clean beach, delicious food...",
"negativeFeedback":"WiFi was slow in the rooms",
"visitDate":"2024-08-15",
"creationDate":"2024-08-25",
"tripType":"Family with children",
"userAvatar":"https://...",
"managementResponse":"Thank you for your feedback!",
"managementResponseDate":"2024-08-26",
"photos":[{"url":"https://...","caption":"Beach view"}],
"additionalRatings":{"priceQuality":9,"room":8,"location":10,"clean":9,"service":9},
"overview":{
"averageReviewsRating":8.7,
"additionalRatingsAverage":{"room":8.5,"priceQuality":8.8,"location":9.2,"service":8.6,"clean":8.9},
"additionalRatingsCount":{
"room":1234,
"meal":1180,
"priceQuality":1205,
"location":1198,
"service":1167,
"clean":1223
},
"amountBySeasons":{"summer":856,"spring":234,"winter":89,"fall":145},
"amountByRatingType":{"good":1089,"medium":189,"bad":46}
}
}

Dataset & API Access

• Console: Run Output → Dataset → Export (JSON/CSV/Excel)
• API:

$curl"https://api.apify.com/v2/datasets/[DATASET_ID]/items"

Performance, Limits & Tips

• Default: unlimited per hotel (0). Large hotels may have thousands of reviews.
• Typical ranges: small 50–200, medium 200–1000, popular 1000–5000+ reviews.
• Filters reduce result count:
  • With photos: typically 20–40% of reviews
  • Detailed ratings: typically 60–80%
  • Detailed text: typically 40–70%
• Many hotels with high limits → longer runtimes. Add limits if needed.

Integrations

• Webhooks: Trigger on run completion and process data automatically.
• API: Pull data directly with the Dataset API.
• Exports: Download JSON, CSV, Excel, XML, HTML Table.

Use Cases

• Hotel operations: sentiment tracking, complaint/root-cause analysis, competitor comparisons
• Market research: region/brand trend analysis, seasonality insights
• Data science: sentiment analysis, recommender systems, NLP, predictive models

FAQ

What is the Sletat.ru Review Scraper?

An Apify Actor (Sletat scraper) that programmatically extracts Sletat.ru hotel reviews (text, ratings, photos, and metadata) into a structured dataset for analysis and automation using patterns similar to the Sletat Hotel Reviews API.

Do I need to use a proxy?

Not required. The project already includes built-in proxy handling; no additional proxy setup is needed.

Can I filter reviews by sentiment or content?

Yes. Use feedbackType for sentiment (all/positive/negative) and toggle onlyWithPhotos, onlyWithDetailedText, and onlyWithDetailedRatings for content-based filtering.

How many reviews can I scrape per hotel?

Unlimited by default (maxReviewsPerHotel = 0). Set any positive number to cap results per hotel.

What is the output format?

Each review is stored as a JSON item in the Apify Dataset, including hotel info and an overview object with aggregates.

Who is this for?

Teams running reputation management, competitive intelligence, or data science workflows that need reliable Sletat review data at scale.