---
title: "Hotel Review Aggregation & Analysis API"
description: "Aggregate and analyze hotel reviews, ratings, and amenities from booking platforms. Build hospitality datasets as structured JSON with WebScraping.AI."
url: https://webscraping.ai/use-cases/hotel-review-aggregation
markdown_index: https://webscraping.ai/llms.txt
---
TRAVEL & HOSPITALITY

# Hotel Review Aggregation

Aggregate hotel reviews, ratings, and amenity data from booking platforms. Run hotel review analysis to power recommendation engines and market research.

[Start Free Trial](https://webscraping.ai/auth/sign_up)[View Documentation](https://webscraping.ai/docs)

## 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**

```bash
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"]
# }
```

**Python**

```python
# 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"]
# }
```

**JavaScript**

```javascript
// 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**

```php
<?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"]
// }
```

**Ruby**

```ruby
# 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**

```go
// 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"]
// }
```

**Java**

```java
// Maven: ai.webscraping:webscraping-ai:4.2.0
// 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"]
// }
```

**C#**

```csharp
// 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**

```bash
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?"
```

**Python**

```python
# 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)
```

**JavaScript**

```javascript
// 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**

```php
<?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;
```

**Ruby**

```ruby
# 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**

```go
// 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)
}
```

**Java**

```java
// Maven: ai.webscraping:webscraping-ai:4.2.0
// 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);
```

**C#**

```csharp
// 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](https://webscraping.ai/use-cases/travel-price-tracking): Monitor flight and hotel prices.

[Price Monitoring](https://webscraping.ai/use-cases/price-monitoring): Track competitor pricing across markets.

[Data Aggregation](https://webscraping.ai/use-cases/product-data-aggregation): Aggregate data from multiple sources.

## Start Collecting Hotel Data

Get started with 2,000 free API credits. No credit card required.

[Start Free Trial](https://webscraping.ai/auth/sign_up)[View API Documentation](https://webscraping.ai/docs)