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Authentic Worldwide Accommodation Reviews
Product Reviews & Feedback
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About
This collection features 3,705 authentic guest experiences gathered from hotels situated in various countries. The data serves as a valuable resource for analyzing guest sentiments and understanding factors that influence hotel feedback and ratings globally. Collected using web scraping techniques, the quality of the raw data is excellent, and reviews are preserved in their original, untranslated languages. It is highly suitable for trend analysis and deriving insights into the global accommodation landscape.
Columns
- Rating: Represents the satisfaction level provided by the reviewer, typically scored on a scale up to 10.
- Date: The exact publication date when the hotel review was posted.
- Description: Contains the full textual content where guests share their personal opinions, experiences, and feedback regarding their stay.
- Hotel_name: Specifies the name of the unique hotel associated with the review.
- City: Denotes the geographical location of the hotel.
- Country: Indicates the country where the hotel is situated, offering essential contextual information.
Distribution
The data file is structured for use in common formats like CSV, with the included sample file named
global_hotel_reviews.csv (approximately 999.14 kB). It contains six distinct columns and features 3,705 records detailing guest feedback. The data was collected on 22 September 2023. Analysis of the ratings shows a high average satisfaction level, with a mean rating of 8.85 out of 10.Usage
Ideal applications for this data include performing detailed sentiment analysis to gauge public perception of the hospitality sector. It can be used for advanced trend analysis concerning guest expectations and satisfaction metrics. The resource is also applicable for generating synthetic data based on real-world feedback patterns.
Coverage
The dataset spans international locations, providing geographical context via city and country fields. Major coverage examples include data points related to hotels in Mexico (42% of the records) and cities such as Cancun (42%) and Geneva (17%). Reviews are provided in their original languages, offering linguistic diversity.
License
CC0: Public Domain
Who Can Use It
Researchers and Analysts: Utilise the data for academic studies or market research concerning customer experience and hospitality trends.
Data Scientists: Apply machine learning models for natural language processing, sentiment scoring, and generating new data based on observed patterns.
Dataset Name Suggestions
- International Hotel Reviews
- Global Guest Feedback Archive
- Authentic Worldwide Accommodation Reviews
- Multilingual Hospitality Feedback
Attributes
Original Data Source:Authentic Worldwide Accommodation Reviews
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