OYO Rooms Reviews Scraper: Extract Hotel Reviews & Ratings

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What Is OYO Rooms?

OYOrooms.com is one of Asia's largest budget hotel chains and booking platforms, operating thousands of properties across India, Southeast Asia, and beyond. Guest reviews on OYO carry significant weight for booking decisions and brand reputation. Manually collecting this feedback at scale is impractical — the OYO Rooms Reviews Scraper automates extraction, delivering structured review data ready for analysis.

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Overview

The OYOrooms.com Reviews Scraper targets a specific hotel's review section by ID, pulling guest feedback with flexible sorting and pagination controls. It suits:

• Hotel managers monitoring guest sentiment
• Reputation management teams tracking rating trends
• Market researchers benchmarking properties
• Data analysts building hospitality sentiment datasets

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Input Format

The scraper uses a simple JSON configuration — no URLs required, just a hotel ID:

{
  "hotel_id": "20146",
  "sort_by": "rating-low",
  "offset": 30,
  "max_items_per_url": 200,
  "ignore_url_failures": true
}

Input Fields

Field	Type	Description
hotel_id	string	The numeric ID from the OYO hotel page URL. Example: https://www.oyorooms.com/20146/ → "20146"
sort_by	string	Controls review ordering. Options: "relevance" (default), "date-high" (newest first), "rating-high" (highest rating first), "rating-low" (lowest rating first)
offset	integer	Starting position for pagination. Set to 0 to begin from the first review; set to 30 to skip the first 30 entries. Useful for resuming or paginating large datasets
max_items_per_url	integer	Maximum number of reviews to collect per run. Default: 20, configurable up to 200 or more
ignore_url_failures	boolean	If true, the scraper continues running when errors occur rather than stopping entirely. Recommended for bulk runs

Tip: To collect the most critical feedback first, use "sort_by": "rating-low". For reputation monitoring, use "date-high" to surface the newest reviews.

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Output Format

Sample Output Record

{
  "review_id": 444914740,
  "user_name": "Rabiya",
  "user_image": "usr.image",
  "review_text": "A very nice hotel in banglore.\nThe reception gay is very friendly and helpful. Rooms are neat and clean.. specially washroom was very clean all was very good and nice..\nA very good hotel with a comfortable price...",
  "date": "11 Nov 2025",
  "rating": {
    "bg_color": "#346000",
    "title": "5"
  },
  "user_image_feedback_list": null,
  "from_url": "https://www.oyorooms.com/api/pwa/updateHotelCall?url=https%3A%2F%2Fbff.oyorooms.com%2Fv1%2Fhotels%2Freviews%3Fovh_property%3Dfalse%26ai_generated_tags_flow%3Dfalse%26limit%3D30%26hotel_id%3D20146&offset=30&source=Web+Booking&user_mode%5B%5D=Consumer_Guest&sort_option=relevance_desc"
}

Each review returns a record with 7 fields:

Field	Meaning
Review ID	Unique identifier for the review on OYO's platform
User Name	Display name of the guest who submitted the review
User Image	URL of the reviewer's profile avatar (may be null if not set)
Review Text	Full text of the guest's written feedback
Date	Submission date of the review
Rating	Numeric score given by the guest (typically on a 1–5 scale)
User Image Feedback List	Array of photo URLs uploaded by the reviewer alongside their feedback

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How to Use

1. Find the hotel ID — Open the OYO hotel page (e.g., https://www.oyorooms.com/20146/). The number in the URL is the hotel_id.
2. Configure sorting — Choose sort_by based on your goal: sentiment analysis → rating-low; recency monitoring → date-high.
3. Set pagination — Use offset to paginate across large review sets. For example, run with offset: 0, then offset: 200, etc.
4. Set item limit — Adjust max_items_per_url based on how many reviews you need.
5. Run and export — Download results as JSON or CSV for analysis.

Common issues:

• If User Image or User Image Feedback List is empty, the reviewer did not upload a photo — this is expected behavior.
• If the hotel has fewer reviews than your max_items_per_url, the scraper returns whatever is available.

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Use Cases & Business Value

• Sentiment analysis: Feed review text into NLP pipelines to classify guest satisfaction themes
• Reputation tracking: Monitor rating distribution over time for a property
• Competitive benchmarking: Compare review patterns across multiple OYO properties
• Training data: Build labeled datasets for hospitality-focused ML models using ratings + text pairs

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Conclusion

The OYO Rooms Reviews Scraper makes it straightforward to collect structured guest feedback from any OYO property at scale. With sorting, pagination, and photo URL extraction built in, it delivers everything needed for reputation analysis, sentiment research, and competitive intelligence — in a single, clean dataset.