Sletat Review Scraper

Sletat Review Scraper

by tufantoksoz

This scraper collects hotel reviews, ratings, and user feedback from Sletat.ru. Fast, reliable, and ideal for analysis, market research, competitor mo...

32 runs
3 users
Try This Actor

Opens on Apify.com

About Sletat Review Scraper

This scraper collects hotel reviews, ratings, and user feedback from Sletat.ru. Fast, reliable, and ideal for analysis, market research, competitor monitoring, and travel data insights

What does this actor do?

Sletat Review Scraper is a web scraping and automation tool available on the Apify platform. It's designed to help you extract data and automate tasks efficiently in the cloud.

Key Features

  • Cloud-based execution - no local setup required
  • Scalable infrastructure for large-scale operations
  • API access for integration with your applications
  • Built-in proxy rotation and anti-blocking measures
  • Scheduled runs and webhooks for automation

How to Use

  1. Click "Try This Actor" to open it on Apify
  2. Create a free Apify account if you don't have one
  3. Configure the input parameters as needed
  4. Run the actor and download your results

Documentation

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 | Field | Type | Required | Default | Description | | ------------------------- | ------------- | -------- | ------- | ----------------------------------------------- | | startUrls | array | Yes | - | List of Sletat.ru hotel page URLs to scrape | | maxReviewsPerHotel | integer | No | 0 | Max reviews per hotel (0 = All Reviews) | | onlyWithPhotos | boolean | No | false | Only reviews with photos | | onlyWithDetailedText | boolean | No | false | Only reviews with detailed text | | onlyWithDetailedRatings | boolean | No | false | Only reviews with category-level ratings | | feedbackType | string | No | "all" | Sentiment filter: all, positive, negative | ### Input Example json { "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 json { "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: bash 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.

Categories

TRAVEL AUTOMATION DEVELOPER_TOOLS

Common Use Cases

Market Research

Gather competitive intelligence and market data

Lead Generation

Extract contact information for sales outreach

Price Monitoring

Track competitor pricing and product changes

Content Aggregation

Collect and organize content from multiple sources

Ready to Get Started?

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Actor Information

Developer
tufantoksoz
Pricing
Paid
Total Runs
32
Active Users
3

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