Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world. This dataset describes the listing activity and metrics in London UK in 2022.

This public dataset is part of Airbnb, and the original source can be found Here

Inspiration

• What can we learn about different hosts and areas?
• What can we learn from predictions? (ex: locations, prices, reviews, etc)
• Which hosts are the busiest and why?
• Is there any noticeable difference of traffic among different areas and what could be the reason for it?

The Airbnb Reviews dataset provides structured, multilingual guest feedback from Airbnb listings worldwide. Each entry includes the full review text, star rating, reviewer profile, location, timestamp, and language. Ideal for sentiment analysis, reputation monitoring, travel market research, and AI/ML training, this dataset allows country-level filtering for the US, EU, and Australia. Continuously updated and scalable to millions of reviews, the data is exportable in CSV, JSON, or JSONL formats. It is ready for analytics pipelines, NLP applications, recommendation engines, and travel trend analysis.

You can request the large dataset at: Airbnb Reviews
To get a custom data quote, visit: Get quote

Key Features:

• Full review text with star ratings and verified stay flags.

• Metadata including reviewer name, profile, property type, location, timestamp, and language.

• Multilingual coverage for global analysis.

• Country-specific filtering for US, EU, and AU markets.

• Continuous updates to include new reviews and listings.

• Export formats: CSV, JSON, JSONL.

• Scalable for millions of reviews.

Use Cases:

• Train AI models for sentiment analysis and review classification.

• Monitor property and host reputation across regions.

• Build semantic search engines for Airbnb reviews.

• Conduct global or regional travel market research.

• Feed review summarizers and QA models with structured review data.

Data was downloaded from: http://insideairbnb.com/get-the-data.html Data was compiled on 31 August, 2019

Files description: - listings_detailed.csv - Detailed Listings data for Stockholm - reviews_detailed.csv - Detailed Review Data for listings in Stockholm - listings.csv - Summary information and metrics for listings in Stockholm (good for visualisations). - reviews.csv - Summary Review data and Listing ID (to facilitate time based analytics and visualisations linked to a listing).

Dataset

This dataset was created by Fuyutaro Suzuki

Contents

Boston Airbnb Reviews

Insights into Customer Satisfaction

By Marcos Dias [source]

About this dataset

This dataset from Boston Air BnB provides you with an in-depth look into the experiences of past customers and insightful reviews about their stay. It includes detailed information such as the date of the review, reviewer name, their specific comments, and more! Allowing you to better understand each customer's personal opinion on a property, this dataset is perfect for those who wish to gain a comprehensive understanding of what makes AirBNB experiences so special. Not only do real estate investors have access to important data points like price and location, but they can now gain insider information on what made past customers truly happy with their stay - that could mean all the difference in building a successful business! Dive into this remarkable collection of reviews now and get ready to experience it yourself soon

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How to use the dataset

This dataset of Boston AirBNB Reviews provides detailed insights into the guest experience with a wide range of AirBNBs in Boston. It provides key information that can be used to identify areas of strength and improvement when developing hospitality strategies in this area.

Research Ideas

• Using the reviewer name and comments to create a sentiment analysis tool that rate hosts and listings based on customers' experience, helping potential guests make informed choices when booking.
• Combining data from the reviews with other data such as prices, availability, and amenities for Airbnb users to enable customers to compare cost efficiency with specific needs in one platform.
• Analyzing the comments made by reviewers over time in order to track trends of customer concerns or feedback allowing Airbnb host to adjust their services accordingly

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: reviews.csv | Column name | Description | |:------------------|:---------------------------------------------------------------| | date | Date of the review submission. (Date) | | reviewer_name | Name of the reviewer. (String) | | comments | Comments made by the reviewer about their experience. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Marcos Dias.

this graph was created in PowerBi, Loocker Studio and R :

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This dataset provides a snapshot of Airbnb listings across major Italian cities offering valuable insights into the short-term rental market in Italy Whether you're interested in pricing trends impact of seasonality superhost classification this dataset has something for you Data refer to a period between September 2023 and September 2024 Key Features City-level data Explore listings in popular cities like Florence Milan Naples Rome and Venice Comprehensive metrics Data includes pricing review scores host details and more Seasonal analysis Data spans different periods allowing for comparisons across seasons Data Dictionary Listings id Unique identifier for each listing Last year reviews Number of reviews received in the twelve months before the scraping data Date of scraping Host since Date the host joined Airbnb Host is superhost Whether the host is a simple host or a superhost Host number of listings Total number of listings the host has Neighbourhood Neighborhood where the listing is located Beds number Bedrooms number Property type Type of room (e.g., entire home private room) Maximum allowed guests Number of guests the listing can accommodate Price Price per night (in Euro) Total reviews Total number of reviews Rating score Overall rating of the listing Accuracy score Accuracy rating Cleanliness score Cleanliness rating Checkin score Check-in rating Communication score Communication rating Location score Location rating Value for money score Value rating Reviews per month Number of reviews per month City Season Time period when the data was scraped (e.g., Early Winter) Bathrooms number Number of bathrooms Bathrooms type Type of bathrooms (shared vs private) Coordinates latitude longitude For visualization reason it is also provide a csv with all city neighbourhoods and the relative geojson Disclaimer This dataset is intended for informational and research purposes only It is not affiliated with Airbnb or any other organization.

Our travel datasets provide extensive, structured data covering various aspects of the global travel and hospitality industry. These datasets are ideal for businesses, analysts, and developers looking to gain insights into hotel pricing, short-term rentals, restaurant listings, and travel trends. Whether you're optimizing pricing strategies, analyzing market trends, or enhancing travel-related applications, our datasets offer the depth and accuracy you need.

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Dataset

This dataset was created by Vahid Nasiri

Contents

Context and Methodology:
This dataset was created as part of a machine learning project for predicting Airbnb rental prices in the USA (year 2023). It is used to evaluate models (in particular a RandomForestRegressor) on training and test data that were previously cleaned and processed from a large raw dataset (AB_US_2023.csv). The training data were versioned in DBRepo and loaded into the Modeling_Regression.ipynb notebook via the API. A 10-fold cross-validation tuning on the training data optimizes the model’s three hyperparameters.

Technical Details:
The dataset is split into a training set and a test set. All output files (model pickle, metrics CSV, plots) are stored in the results/ folder at the repository root. Additional configuration and sample data can be found in data/sampled_data/. The notebook retrieves only the current training and test PIDs via the DBRepo API, so no local CSVs are versioned in the repo.

Further Notes:
A detailed setup guide (downloading large CSVs, adjusting paths) and all other preprocessing notebooks are documented in the README. License: CC-BY-4.0.