TRAVEL & HOSPITALITY

Hotel Review Aggregation

Collect hotel reviews, ratings, and amenity data from booking platforms. Power recommendation engines and market analysis.

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Hotel Data is Scattered

Travelers rely on reviews to make booking decisions. Building a comprehensive view of hotel quality requires aggregating data from multiple booking platforms and review sites.

Each platform has different formats, anti-scraping measures, and data structures. You need a reliable way to collect and normalize this data.

WebScraping.AI Solution

  • Multi-Platform Collection: Extract from booking sites, review platforms, and OTAs
  • AI-Powered Extraction: Intelligently parse any review format
  • Sentiment Analysis: Understand what guests love and dislike
  • Structured Output: Clean, normalized data ready for your database

Hotel Data Points

Comprehensive hospitality intelligence

Ratings & Reviews

Overall ratings, category scores, review counts, and individual reviews.

Amenities

Pool, gym, WiFi, breakfast, parking, and all available amenities.

Location Data

Address, neighborhood, nearby attractions, and location scores.

Pricing

Room rates, price ranges, and seasonal pricing data.

Code Examples

Extract hotel review data

Extract hotel data from a booking platform
curl -G "https://api.webscraping.ai/ai/fields" \
  --data-urlencode "api_key=YOUR_API_KEY" \
  --data-urlencode "url=https://booking-platform.com/hotel/grand-resort-miami" \
  --data-urlencode "fields[hotel_name]=Name of the hotel" \
  --data-urlencode "fields[star_rating]=Hotel star rating (1-5)" \
  --data-urlencode "fields[guest_rating]=Guest review rating" \
  --data-urlencode "fields[total_reviews]=Number of reviews" \
  --data-urlencode "fields[location]=Hotel address and area" \
  --data-urlencode "fields[price_range]=Typical price range per night" \
  --data-urlencode "fields[amenities]=List of amenities offered" \
  --data-urlencode "fields[cleanliness_score]=Cleanliness rating if shown" \
  --data-urlencode "fields[service_score]=Service rating if shown" \
  --data-urlencode "fields[location_score]=Location rating if shown" \
  --data-urlencode "fields[value_score]=Value for money rating if shown" \
  --data-urlencode "fields[highlights]=Main highlights or selling points"
# Response:
# {
#   "hotel_name": "Grand Resort Miami Beach",
#   "star_rating": 4,
#   "guest_rating": 8.7,
#   "total_reviews": 2341,
#   "location": "Collins Avenue, Miami Beach, FL",
#   "price_range": "$250-$450/night",
#   "amenities": ["Pool", "Spa", "Beach Access", "Free WiFi", "Restaurant"],
#   "cleanliness_score": 9.1,
#   "service_score": 8.5,
#   "location_score": 9.4,
#   "value_score": 8.2,
#   "highlights": ["Beachfront location", "Rooftop pool", "Award-winning restaurant"]
# }
# pip install webscraping_ai
# https://pypi.org/project/webscraping-ai/
from webscraping_ai import Client

client = Client(api_key="YOUR_API_KEY")
result = client.fields(
    "https://booking-platform.com/hotel/grand-resort-miami",
    fields={
        "hotel_name": "Name of the hotel",
        "star_rating": "Hotel star rating (1-5)",
        "guest_rating": "Guest review rating",
        "total_reviews": "Number of reviews",
        "location": "Hotel address and area",
        "price_range": "Typical price range per night",
        "amenities": "List of amenities offered",
        "cleanliness_score": "Cleanliness rating if shown",
        "service_score": "Service rating if shown",
        "location_score": "Location rating if shown",
        "value_score": "Value for money rating if shown",
        "highlights": "Main highlights or selling points",
    },
)
print(result)
# Response:
# {
#   "hotel_name": "Grand Resort Miami Beach",
#   "star_rating": 4,
#   "guest_rating": 8.7,
#   "total_reviews": 2341,
#   "location": "Collins Avenue, Miami Beach, FL",
#   "price_range": "$250-$450/night",
#   "amenities": ["Pool", "Spa", "Beach Access", "Free WiFi", "Restaurant"],
#   "cleanliness_score": 9.1,
#   "service_score": 8.5,
#   "location_score": 9.4,
#   "value_score": 8.2,
#   "highlights": ["Beachfront location", "Rooftop pool", "Award-winning restaurant"]
# }
// npm install webscraping-ai
// https://www.npmjs.com/package/webscraping-ai
import { WebScrapingAI } from 'webscraping-ai';

const client = new WebScrapingAI({ apiKey: 'YOUR_API_KEY' });
const result = await client.fields({
  url: 'https://booking-platform.com/hotel/grand-resort-miami',
  fields: {
    hotel_name: 'Name of the hotel',
    star_rating: 'Hotel star rating (1-5)',
    guest_rating: 'Guest review rating',
    total_reviews: 'Number of reviews',
    location: 'Hotel address and area',
    price_range: 'Typical price range per night',
    amenities: 'List of amenities offered',
    cleanliness_score: 'Cleanliness rating if shown',
    service_score: 'Service rating if shown',
    location_score: 'Location rating if shown',
    value_score: 'Value for money rating if shown',
    highlights: 'Main highlights or selling points',
  },
});
console.log(result);
// Response:
// {
//   "hotel_name": "Grand Resort Miami Beach",
//   "star_rating": 4,
//   "guest_rating": 8.7,
//   "total_reviews": 2341,
//   "location": "Collins Avenue, Miami Beach, FL",
//   "price_range": "$250-$450/night",
//   "amenities": ["Pool", "Spa", "Beach Access", "Free WiFi", "Restaurant"],
//   "cleanliness_score": 9.1,
//   "service_score": 8.5,
//   "location_score": 9.4,
//   "value_score": 8.2,
//   "highlights": ["Beachfront location", "Rooftop pool", "Award-winning restaurant"]
// }
<?php
// composer require webscraping-ai/webscraping-ai-php
// https://packagist.org/packages/webscraping-ai/webscraping-ai-php
require 'vendor/autoload.php';

use WebScrapingAI\Client;

$client = new Client('YOUR_API_KEY');
$result = $client->fields('https://booking-platform.com/hotel/grand-resort-miami', [
    'hotel_name'        => 'Name of the hotel',
    'star_rating'       => 'Hotel star rating (1-5)',
    'guest_rating'      => 'Guest review rating',
    'total_reviews'     => 'Number of reviews',
    'location'          => 'Hotel address and area',
    'price_range'       => 'Typical price range per night',
    'amenities'         => 'List of amenities offered',
    'cleanliness_score' => 'Cleanliness rating if shown',
    'service_score'     => 'Service rating if shown',
    'location_score'    => 'Location rating if shown',
    'value_score'       => 'Value for money rating if shown',
    'highlights'        => 'Main highlights or selling points',
]);
print_r($result);
// Response:
// {
//   "hotel_name": "Grand Resort Miami Beach",
//   "star_rating": 4,
//   "guest_rating": 8.7,
//   "total_reviews": 2341,
//   "location": "Collins Avenue, Miami Beach, FL",
//   "price_range": "$250-$450/night",
//   "amenities": ["Pool", "Spa", "Beach Access", "Free WiFi", "Restaurant"],
//   "cleanliness_score": 9.1,
//   "service_score": 8.5,
//   "location_score": 9.4,
//   "value_score": 8.2,
//   "highlights": ["Beachfront location", "Rooftop pool", "Award-winning restaurant"]
// }
# gem install webscraping_ai
# https://rubygems.org/gems/webscraping_ai
require 'webscraping_ai'

client = WebScrapingAI::Client.new(api_key: 'YOUR_API_KEY')
result = client.fields(
  'https://booking-platform.com/hotel/grand-resort-miami',
  fields: {
    hotel_name:         'Name of the hotel',
    star_rating:        'Hotel star rating (1-5)',
    guest_rating:       'Guest review rating',
    total_reviews:      'Number of reviews',
    location:           'Hotel address and area',
    price_range:        'Typical price range per night',
    amenities:          'List of amenities offered',
    cleanliness_score:  'Cleanliness rating if shown',
    service_score:      'Service rating if shown',
    location_score:     'Location rating if shown',
    value_score:        'Value for money rating if shown',
    highlights:         'Main highlights or selling points',
  }
)
puts result.inspect
# Response:
# {
#   "hotel_name": "Grand Resort Miami Beach",
#   "star_rating": 4,
#   "guest_rating": 8.7,
#   "total_reviews": 2341,
#   "location": "Collins Avenue, Miami Beach, FL",
#   "price_range": "$250-$450/night",
#   "amenities": ["Pool", "Spa", "Beach Access", "Free WiFi", "Restaurant"],
#   "cleanliness_score": 9.1,
#   "service_score": 8.5,
#   "location_score": 9.4,
#   "value_score": 8.2,
#   "highlights": ["Beachfront location", "Rooftop pool", "Award-winning restaurant"]
# }
// go get github.com/webscraping-ai/webscraping-ai-go/v4
// https://pkg.go.dev/github.com/webscraping-ai/webscraping-ai-go/v4
package main

import (
    "context"
    "fmt"

    webscrapingai "github.com/webscraping-ai/webscraping-ai-go/v4"
)

func main() {
    client, _ := webscrapingai.NewClient(&webscrapingai.Config{APIKey: "YOUR_API_KEY"})
    result, _ := client.Fields(context.Background(), &webscrapingai.FieldsOptions{
        URL: "https://booking-platform.com/hotel/grand-resort-miami",
        Fields: map[string]string{
            "hotel_name": "Name of the hotel",
            "star_rating": "Hotel star rating (1-5)",
            "guest_rating": "Guest review rating",
            "total_reviews": "Number of reviews",
            "location": "Hotel address and area",
            "price_range": "Typical price range per night",
            "amenities": "List of amenities offered",
            "cleanliness_score": "Cleanliness rating if shown",
            "service_score": "Service rating if shown",
            "location_score": "Location rating if shown",
            "value_score": "Value for money rating if shown",
            "highlights": "Main highlights or selling points",
        },
    })
    fmt.Println(result.Result)
}
// Response:
// {
//   "hotel_name": "Grand Resort Miami Beach",
//   "star_rating": 4,
//   "guest_rating": 8.7,
//   "total_reviews": 2341,
//   "location": "Collins Avenue, Miami Beach, FL",
//   "price_range": "$250-$450/night",
//   "amenities": ["Pool", "Spa", "Beach Access", "Free WiFi", "Restaurant"],
//   "cleanliness_score": 9.1,
//   "service_score": 8.5,
//   "location_score": 9.4,
//   "value_score": 8.2,
//   "highlights": ["Beachfront location", "Rooftop pool", "Award-winning restaurant"]
// }
// Maven: ai.webscraping:webscraping-ai:4.0.0
// https://central.sonatype.com/artifact/ai.webscraping/webscraping-ai
import ai.webscraping.Client;
import ai.webscraping.Config;
import ai.webscraping.option.FieldsOptions;
import ai.webscraping.result.FieldsResult;

Client client = new Client(Config.builder().apiKey("YOUR_API_KEY").build());
FieldsResult result = client.fields(FieldsOptions.builder()
    .url("https://booking-platform.com/hotel/grand-resort-miami")
    .addField("hotel_name", "Name of the hotel")
    .addField("star_rating", "Hotel star rating (1-5)")
    .addField("guest_rating", "Guest review rating")
    .addField("total_reviews", "Number of reviews")
    .addField("location", "Hotel address and area")
    .addField("price_range", "Typical price range per night")
    .addField("amenities", "List of amenities offered")
    .addField("cleanliness_score", "Cleanliness rating if shown")
    .addField("service_score", "Service rating if shown")
    .addField("location_score", "Location rating if shown")
    .addField("value_score", "Value for money rating if shown")
    .addField("highlights", "Main highlights or selling points")
    .build());
System.out.println(result.getResult());
// Response:
// {
//   "hotel_name": "Grand Resort Miami Beach",
//   "star_rating": 4,
//   "guest_rating": 8.7,
//   "total_reviews": 2341,
//   "location": "Collins Avenue, Miami Beach, FL",
//   "price_range": "$250-$450/night",
//   "amenities": ["Pool", "Spa", "Beach Access", "Free WiFi", "Restaurant"],
//   "cleanliness_score": 9.1,
//   "service_score": 8.5,
//   "location_score": 9.4,
//   "value_score": 8.2,
//   "highlights": ["Beachfront location", "Rooftop pool", "Award-winning restaurant"]
// }
// dotnet add package WebScrapingAI
// https://www.nuget.org/packages/WebScrapingAI
using WebScrapingAI;

var client = new WebScrapingAIClient(new WebScrapingAIClientOptions { ApiKey = "YOUR_API_KEY" });
var result = await client.FieldsAsync(new FieldsRequest {
    Url = "https://booking-platform.com/hotel/grand-resort-miami",
    Fields = new Dictionary<string, string> {
        ["hotel_name"] = "Name of the hotel",
        ["star_rating"] = "Hotel star rating (1-5)",
        ["guest_rating"] = "Guest review rating",
        ["total_reviews"] = "Number of reviews",
        ["location"] = "Hotel address and area",
        ["price_range"] = "Typical price range per night",
        ["amenities"] = "List of amenities offered",
        ["cleanliness_score"] = "Cleanliness rating if shown",
        ["service_score"] = "Service rating if shown",
        ["location_score"] = "Location rating if shown",
        ["value_score"] = "Value for money rating if shown",
        ["highlights"] = "Main highlights or selling points",
    },
});
Console.WriteLine(result.Result);
// Response:
// {
//   "hotel_name": "Grand Resort Miami Beach",
//   "star_rating": 4,
//   "guest_rating": 8.7,
//   "total_reviews": 2341,
//   "location": "Collins Avenue, Miami Beach, FL",
//   "price_range": "$250-$450/night",
//   "amenities": ["Pool", "Spa", "Beach Access", "Free WiFi", "Restaurant"],
//   "cleanliness_score": 9.1,
//   "service_score": 8.5,
//   "location_score": 9.4,
//   "value_score": 8.2,
//   "highlights": ["Beachfront location", "Rooftop pool", "Award-winning restaurant"]
// }
Analyze review sentiment for the same property
curl -G "https://api.webscraping.ai/ai/question" \
  --data-urlencode "api_key=YOUR_API_KEY" \
  --data-urlencode "url=https://booking-platform.com/hotel/grand-resort-miami" \
  --data-urlencode "question=What do guests love most about this hotel? What are the most common complaints? Would you recommend it for families, couples, or business travelers?"

# pip install webscraping_ai
# https://pypi.org/project/webscraping-ai/
from webscraping_ai import Client

client = Client(api_key="YOUR_API_KEY")
answer = client.question(
    "https://booking-platform.com/hotel/grand-resort-miami",
    question="What do guests love most about this hotel? What are the most common complaints? Would you recommend it for families, couples, or business travelers?",
)
print(answer)

// npm install webscraping-ai
// https://www.npmjs.com/package/webscraping-ai
import { WebScrapingAI } from 'webscraping-ai';

const client = new WebScrapingAI({ apiKey: 'YOUR_API_KEY' });
const answer = await client.question({
  url: 'https://booking-platform.com/hotel/grand-resort-miami',
  question: 'What do guests love most about this hotel? What are the most common complaints? Would you recommend it for families, couples, or business travelers?',
});
console.log(answer);

<?php
// composer require webscraping-ai/webscraping-ai-php
// https://packagist.org/packages/webscraping-ai/webscraping-ai-php
require 'vendor/autoload.php';

use WebScrapingAI\Client;

$client = new Client('YOUR_API_KEY');
$answer = $client->question(
    'https://booking-platform.com/hotel/grand-resort-miami',
    'What do guests love most about this hotel? What are the most common complaints? Would you recommend it for families, couples, or business travelers?',
);
echo $answer;

# gem install webscraping_ai
# https://rubygems.org/gems/webscraping_ai
require 'webscraping_ai'

client = WebScrapingAI::Client.new(api_key: 'YOUR_API_KEY')
answer = client.question(
  'https://booking-platform.com/hotel/grand-resort-miami',
  question: 'What do guests love most about this hotel? What are the most common complaints? Would you recommend it for families, couples, or business travelers?'
)
puts answer

// go get github.com/webscraping-ai/webscraping-ai-go/v4
// https://pkg.go.dev/github.com/webscraping-ai/webscraping-ai-go/v4
package main

import (
    "context"
    "fmt"

    webscrapingai "github.com/webscraping-ai/webscraping-ai-go/v4"
)

func main() {
    client, _ := webscrapingai.NewClient(&webscrapingai.Config{APIKey: "YOUR_API_KEY"})
    answer, _ := client.Question(context.Background(), &webscrapingai.QuestionOptions{
        URL:      "https://booking-platform.com/hotel/grand-resort-miami",
        Question: "What do guests love most about this hotel? What are the most common complaints? Would you recommend it for families, couples, or business travelers?",
    })
    fmt.Println(answer)
}

// Maven: ai.webscraping:webscraping-ai:4.0.0
// https://central.sonatype.com/artifact/ai.webscraping/webscraping-ai
import ai.webscraping.Client;
import ai.webscraping.Config;
import ai.webscraping.option.QuestionOptions;

Client client = new Client(Config.builder().apiKey("YOUR_API_KEY").build());
String answer = client.question(QuestionOptions.builder()
    .url("https://booking-platform.com/hotel/grand-resort-miami")
    .question("What do guests love most about this hotel? What are the most common complaints? Would you recommend it for families, couples, or business travelers?")
    .build());
System.out.println(answer);

// dotnet add package WebScrapingAI
// https://www.nuget.org/packages/WebScrapingAI
using WebScrapingAI;

var client = new WebScrapingAIClient(new WebScrapingAIClientOptions { ApiKey = "YOUR_API_KEY" });
var answer = await client.QuestionAsync(new QuestionRequest {
    Url = "https://booking-platform.com/hotel/grand-resort-miami",
    Question = "What do guests love most about this hotel? What are the most common complaints? Would you recommend it for families, couples, or business travelers?",
});
Console.WriteLine(answer);

Why Use WebScraping.AI

Multi-Platform: Extract from any booking site or review platform.
AI Understanding: Intelligently parse reviews and extract insights.
Normalized Data: Consistent format across all sources.
Sentiment Analysis: Understand what makes guests happy or unhappy.
Scale Easily: Collect data on thousands of properties.

Use Cases

Travel Platforms

Build comprehensive hotel databases

Recommendation Engines

Power personalized hotel suggestions

Market Research

Analyze hospitality market trends

Competitive Intel

Monitor competitor hotel performance

Related Use Cases

More travel data solutions

Travel Price Tracking

Monitor flight and hotel prices.

Price Monitoring

Track competitor pricing across markets.

Data Aggregation

Aggregate data from multiple sources.

Start Collecting Hotel Data

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