Key Travel App StatisticsTop Travel AppsTravel App Market LandscapeTravel App RevenueTravel Revenue By AppTravel App UsersTravel App Market Share United StatesTravel App DownloadsThe online travel...

Tourism Revenues in the United States decreased to 20626 USD Million in July from 20913 USD Million in June of 2025. This dataset provides - United States Tourism Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context: This dataset contains flight fare data that was collected from the EaseMyTrip website using web scraping techniques. The data was collected with the goal of providing users with information that could help them make informed decisions about when and where to purchase flight tickets. By analyzing patterns in flight fares over time, users can identify the best times to book tickets and potentially save money.

Sources: 1. Data collected using Python script with Beautiful Soup and Selenium libraries. 2. Script collected data on various flight details such as Date of booking, Date of travel, Airline and class, Departure time and source, Arrival time and destination, Duration, Total stops, Price. 3. The scraping process was designed to collect data for flights departing from a specific set of airports (Top 7 busiest airports in India). Note that the Departure Time feature also includes the Source airport, and the Arrival Time feature also includes the Destination airport. Which is later extracted in Cleaned_dataset. Also both cleaned and scraped datasets have provided so that one can use dataset as per their requirement and convenience.

Inspiration: 1. Dataset created to provide users with valuable resource for analyzing flight fares in India. 2. Detailed information on flight fares over time can be used to develop more accurate pricing models and inform users about best times to book tickets. 3. Data can also be used to study trends and patterns in the travel industry through air can act as a valuable resource for researchers and analysts.

Limitations: 1. This dataset only covers flights departing from specific airports and limited to a certain time period. 2. To perform time series analysis one have gather data for at least top 10 busiest airports for 365 days. 3. This does not cover variations in aviation fuel prices as this is the one of influencing factor for deciding fare, hence the same dataset might not be useful for next year, but I will try to update it twice in an year. 4. Also demand and supply for the particular flight seat is not available in the dataset as this data is not publicly available on any flight booking web site.

Scope of Improvement: 1. The dataset could be enhanced by including additional features such as current aviation fuel prices and the distance between the source and destination in terms of longitude and latitude. 2. The data could also be expanded to include more airlines and more airports, providing a more comprehensive view of the flight market. 3. Additionally, it may be helpful to include data on flight cancellations, delays, and other factors that can impact the price and availability of flights. 4. Finally, while the current dataset provides information on flight prices, it does not include information on the quality of the flight experience, such as legroom, in-flight amenities, and customer reviews. Including this type of data could provide a more complete picture of the flight market and help travelers make more informed decisions.

A quick view of the CSV file reveals that it contains customer reviews for the top 10 rated airlines in 2023, including Singapore Airlines, Qatar Airways, ANA (All Nippon Airways), Emirates, Japan Airlines, Turkish Airlines, Air France, Cathay Pacific Airways, EVA Air, and Korean Air. It provides insights into passenger satisfaction and service quality aspects, ranging from seat comfort to inflight entertainment.

The dataset consists of 8,100 reviews with 17 columns, including both numerical and categorical data. Here is a brief overview of the columns:

Title: Title of the review. Name: Name of the reviewer. Review Date: Date when the review was posted. Airline: Airline being reviewed. Verified: Whether the review is verified. Reviews: Text of the review. Type of Traveller: Type of traveler (e.g., Solo Leisure, Family Leisure). Month Flown: Month of the flight. Route: Route of the flight. Class: Class of travel (e.g., Economy Class, Business Class). Seat Comfort: Rating for seat comfort (1-5). Staff Service: Rating for staff service (1-5). Food & Beverages: Rating for food and beverages (1-5). Inflight Entertainment: Rating for inflight entertainment (1-5). Value For Money: Rating for value for money (1-5). Overall Rating: Overall rating for the flight (1-10). Recommended: Whether the reviewer recommends the airline (yes/no)

Many people enjoy traveling. When comparing the long distance train ticket users in selected countries worldwide, the highest share can be found in India, where 51 percent of consumers fall into this category. Finland ranks second with 42 percent of respondents being part of this category as well.Statista Consumer Insights offer you all results of our exclusive Statista surveys, based on more than 2,000,000 interviews.

Upvote🙏

The Airlines Reviews and Ratings dataset is a comprehensive collection of passenger feedback on various aspects of their flight experiences across different airlines. This dataset aims to provide insights into passenger satisfaction and airlines' service quality, offering valuable data for analysis in the travel and hospitality industry, customer service improvement, and predictive modeling for customer satisfaction. Airlines Reviews and Ratings Dataset, a rich collection designed to explore the multifaceted aspects of air travel experiences across various airlines worldwide. This dataset encompasses a broad range of data points, from aircraft types and user reviews to detailed service ratings, offering a unique lens through which to analyze and predict airline performance from a passenger perspective.

Column Details:

• Aircraft Type: Type of aircraft used for the flight.
• Users Reviews: Textual reviews provided by the users.
• Country: The country of the airline or the flight origin/destination.
• Type of Travellers: Categorizes travellers (e.g., Solo, Family, Business...).
• Route: The flight route taken.
• Seat Types: Class of the seat (Economy, Business, First Class...).
• Seat Comfort: Rating of the seat comfort.
• Date Flown: When the flight took place./The flight date.
• Cabin Staff Service: Rating of the service provided by the cabin staff.
• Ground Service/Floor: Rating of the ground service, including check-in and boarding.
• Food & Beverages: Rating of the food and beverage quality.
• Wifi & Connectivity: Rating of the wifi and connectivity options available.
• Inflight Entertainment: Rating of the inflight entertainment options.
• Value For Money: Overall value for money rating.
• Recommended: Whether the reviewer recommends the airline or not.

The total consumer spending on restaurants and hotels in Latvia was forecast to continuously increase between 2024 and 2029 by in total 279.5 million U.S. dollars (+19.42 percent). After the ninth consecutive increasing year, the restaurants- and hotels-related spending is estimated to reach 1.7 billion U.S. dollars and therefore a new peak in 2029. Consumer spending, in this case concerning restaurants and hotels, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs) in the selected region. Spending by corporations or the state is not included. Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group 11. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.Find more key insights for the total consumer spending on restaurants and hotels in countries like Lithuania and Estonia.

Adults in the United States were surveyed about the tourist attractions that they viewed most positively as of the third quarter of 2024. The Grand Canyon National Park ranked first on the list with a popularity score amounting to 81 percent. Meanwhile, the Statue of Liberty and Niagra Falls ranked second and third, respectively.

Tourism Revenues in Egypt increased to 14.40 USD Billion in 2024 from 13.60 USD Billion in 2023. This dataset provides - Egypt Tourism Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Tourism Revenues in Turkey increased to 24300 USD Million in the third quarter of 2025 from 16280 USD Million in the second quarter of 2025. This dataset provides - Turkey Tourism Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Wallet-as-a-Service for Travel Market Outlook

According to our latest research, the global Wallet-as-a-Service for Travel market size stood at USD 1.78 billion in 2024, demonstrating robust momentum driven by digital transformation across the travel sector. The market is forecasted to reach USD 7.41 billion by 2033, expanding at a compelling CAGR of 17.2% during the 2025-2033 period. The key growth factor fueling this surge is the increasing demand for seamless, secure, and integrated payment solutions among travelers and travel service providers worldwide, as per our latest market research analysis.

The most significant growth driver for the Wallet-as-a-Service for Travel market is the rapid digitalization of the global travel industry. As travel businesses strive to enhance customer experiences, digital wallets have emerged as a crucial enabler, offering frictionless transactions, instant settlements, and multi-currency support. The proliferation of smartphones and mobile-first consumer behavior further accelerates the adoption of Wallet-as-a-Service platforms, making them indispensable for both travelers and travel operators. Moreover, the increasing preference for contactless payments, especially in the post-pandemic landscape, has elevated the importance of secure and efficient wallet solutions in travel, leading to higher integration rates across airlines, hotels, and online travel platforms.

Another key factor contributing to market expansion is the evolution of loyalty and rewards programs in the travel sector. Wallet-as-a-Service platforms allow travel agencies, airlines, and hotels to seamlessly embed loyalty points, rewards, and promotional offers within digital wallets, thereby enhancing customer engagement and retention. This seamless integration not only streamlines the redemption process for end-users but also provides travel businesses with valuable data insights to personalize offerings. The ability to unify payments, rewards, and identity verification in a single digital wallet is transforming the way travel companies interact with their customers, fostering brand loyalty and repeat business.

Regulatory advancements and the growing emphasis on compliance and security are also propelling the Wallet-as-a-Service for Travel market. Governments and regulatory bodies worldwide are implementing stringent data protection and anti-money laundering (AML) standards, compelling travel businesses to adopt secure wallet solutions that ensure regulatory compliance. The integration of advanced security features such as biometric authentication, tokenization, and real-time fraud detection within Wallet-as-a-Service platforms is instilling greater confidence among both businesses and consumers. This regulatory push, combined with the need for operational efficiency and cost optimization, is driving travel companies to transition from legacy payment systems to agile, cloud-based wallet services.

From a regional perspective, Asia Pacific is leading the Wallet-as-a-Service for Travel market, accounting for the largest share in 2024, followed by North America and Europe. The dominance of Asia Pacific can be attributed to the regionÂ’s burgeoning middle class, high smartphone penetration, and dynamic travel ecosystem, particularly in countries like China, India, and Southeast Asia. North America and Europe are also witnessing significant growth, driven by mature travel markets, advanced digital infrastructure, and strong adoption of fintech solutions among travel operators. Meanwhile, Latin America and the Middle East & Africa are emerging as high-potential regions, fueled by increasing investments in travel technology and digital payment infrastructure.

In recent years, Tokenized Payments in Travel have emerged as a transformative force within the industry, offering enhanced security and convenience for both travelers and service providers. Tokenization replaces sensitive payment information with unique identifiers, or tokens, that are useless if intercepted by malicious actors. This technology significantly reduces the risk of fraud and data breaches, which is particularly crucial in the travel sector where transactions often occur across multiple platforms and international borders. By adopting tokenized payment solutions, travel companies can ensure that customer data is protected, fostering trus

Sentiment Analyses of City Hotels

Techniques, Applications, and Interpretations

About this dataset

Looking for a large, balanced dataset for Sentiment Analysis? Look no further than the reviews of hotels in Los Angeles, New York, and Orlando from booking.com! This dataset contains reviews scraped from the popular travel site, booking.com, and organized into three separate csv files, one for each city. Whether you're interested in analyzing customer sentiment or finding the most frequently used keywords in reviews, this dataset has you covered. So what are you waiting for? Start exploring today!

How to use the dataset

To use this dataset for Sentiment Analysis, you will need to have a basic understanding of Natural Language Processing (NLP) and text data. This dataset consists of reviews of hotels in Los Angeles, New York, and Orlando from the popular travel site, booking.com. The reviews are scraped from the site and organized into three separate csv files, one for each city.

TheAim of this dataset is to provide a fairly large, balanced dataset for Sentiment Analysis. The sentiment of each review is labeled as either positive or negative

Research Ideas

1. Developing a sentiment analysis tool for differentiating positive and negative reviews of hotels

2. Using the reviews to identify which aspects of a hotel are most important to customers

3. Comparing the relative popularity of different hotels in each city

Acknowledgements

This dataset was scraped from booking.com and organized into a csv file by Kaggle user sderosiaux

License

See the dataset description for more information.

Columns

File: la0730.csv | Column name | Description | |:---------------------------|:------------------------------------------------------------------| | name | The name of the hotel. (String) | | Zip code | The zip code of the hotel's location. (String) | | Number of reviewers | The number of reviewers who have reviewed the hotel. (Integer) | | Overall score | The hotel's overall score, on a scale of 1 to 5. (Float) | | Cleanliness | The hotel's cleanliness score, on a scale of 1 to 5. (Float) | | Comfort | The hotel's comfort score, on a scale of 1 to 5. (Float) | | Facilities | The hotel's facilities score, on a scale of 1 to 5. (Float) | | Staff | The hotel's staff score, on a scale of 1 to 5. (Float) | | Value for money | The hotel's value for money score, on a scale of 1 to 5. (Float) | | Free WiFi | Whether or not the hotel offers free WiFi. (Boolean) | | Location | The hotel's location score, on a scale of 1 to 5. (Float) | | good Fitness Center | The hotel's fitness center score, on a scale of 1 to 5. (Float) | | Daily Housekeeping | Whether or not the hotel offers daily housekeeping. (Boolean) | | Heating | The hotel's heating score, on a scale of 1 to 5. (Float) | | Free Parking | Whether or not the hotel offers free parking. (Boolean) | | Airport Shuttle (free) | Whether or not the hotel offers a free airport shuttle. (Boolean) | | Private Parking | Whether or not the hotel offers private parking. (Boolean) | | Spa | The hotel's spa score, on a scale of 1 to 5. (Float) | | On-site Parking | Whether or not the hotel offers on-site parking. (Boolean) |

File: la_5keywords.csv | Column name | Description | |:--------------|:--------------------------------| | name | The name of the hotel. (String) |

File: la_all.csv | Column name | Description | |:---------------------------|:------------------------------------------------------------------| | name | The name of the hotel. (String) | | Zip code | The zip code of the hotel's location. (String) | | Cleanliness | The hotel's cleanliness score, on a scale of 1 to 5. (Float) | | Comfort | The hotel's comfort score, on a scale of 1 to 5. (Float) | | Facilities | The hotel's facilities score, on a scale of 1 to 5. (Float) | | Staff | The hotel's staff score, on a scale of 1 to 5. (Float) | | Value for money | The hotel's value for money score, on a scale of 1 to 5. (Float) | | Free WiFi ...

This is not going to be an article or Op-Ed about Michael Jordan. Since 2009 we've been in the longest bull-market in history, that's 11 years and counting. However a few metrics like the stock market P/E, the call to put ratio and of course the Shiller P/E suggest a great crash is coming in-between the levels of 1929 and the dot.com bubble. Mean reversion historically is inevitable and the Fed's printing money experiment could end in disaster for the stock market in late 2021 or 2022. You can read Jeremy Grantham's Last Dance article here. You are likely well aware of Michael Burry's predicament as well. It's easier for you just to skim through two related videos on this topic of a stock market crash. Michael Burry's Warning see this YouTube. Jeremy Grantham's Warning See this YouTube. Typically when there is a major event in the world, there is a crash and then a bear market and a recovery that takes many many months. In March, 2020 that's not what we saw since the Fed did some astonishing things that means a liquidity sloth and the risk of a major inflation event. The pandemic represented the quickest decline of at least 30% in the history of the benchmark S&P 500, but the recovery was not correlated to anything but Fed intervention. Since the pandemic clearly isn't disappearing and many sectors such as travel, business travel, tourism and supply chain disruptions appear significantly disrupted - the so-called economic recovery isn't so great. And there's this little problem at the heart of global capitalism today, the stock market just keeps going up. Crashes and corrections typically occur frequently in a normal market. But the Fed liquidity and irresponsible printing of money is creating a scenario where normal behavior isn't occurring on the markets. According to data provided by market analytics firm Yardeni Research, the benchmark index has undergone 38 declines of at least 10% since the beginning of 1950. Since March, 2020 we've barely seen a down month. September, 2020 was flat-ish. The S&P 500 has more than doubled since those lows. Look at the angle of the curve: The S&P 500 was 735 at the low in 2009, so in this bull market alone it has gone up 6x in valuation. That's not a normal cycle and it could mean we are due for an epic correction. I have to agree with the analysts who claim that the long, long bull market since 2009 has finally matured into a fully-fledged epic bubble. There is a complacency, buy-the dip frenzy and general meme environment to what BigTech can do in such an environment. The weight of Apple, Amazon, Alphabet, Microsoft, Facebook, Nvidia and Tesla together in the S&P and Nasdaq is approach a ridiculous weighting. When these stocks are seen both as growth, value and companies with unbeatable moats the entire dynamics of the stock market begin to break down. Check out FANG during the pandemic. BigTech is Seen as Bullet-Proof me valuations and a hysterical speculative behavior leads to even higher highs, even as 2020 offered many younger people an on-ramp into investing for the first time. Some analysts at JP Morgan are even saying that until retail investors stop charging into stocks, markets probably don’t have too much to worry about. Hedge funds with payment for order flows can predict exactly how these retail investors are behaving and monetize them. PFOF might even have to be banned by the SEC. The risk-on market theoretically just keeps going up until the Fed raises interest rates, which could be in 2023! For some context, we're more than 1.4 years removed from the bear-market bottom of the coronavirus crash and haven't had even a 5% correction in nine months. This is the most over-priced the market has likely ever been. At the night of the dot-com bubble the S&P 500 was only 1,400. Today it is 4,500, not so many years after. Clearly something is not quite right if you look at history and the P/E ratios. A market pumped with liquidity produces higher earnings with historically low interest rates, it's an environment where dangerous things can occur. In late 1997, as the S&P 500 passed its previous 1929 peak of 21x earnings, that seemed like a lot, but nothing compared to today. For some context, the S&P 500 Shiller P/E closed last week at 38.58, which is nearly a two-decade high. It's also well over double the average Shiller P/E of 16.84, dating back 151 years. So the stock market is likely around 2x over-valued. Try to think rationally about what this means for valuations today and your favorite stock prices, what should they be in historical terms? The S&P 500 is up 31% in the past year. It will likely hit 5,000 before a correction given the amount of added liquidity to the system and the QE the Fed is using that's like a huge abuse of MMT, or Modern Monetary Theory. This has also lent to bubbles in the housing market, crypto and even commodities like Gold with long-term global GDP meeting many headwinds in the years ahead due to a demographic shift of an ageing population and significant technological automation. So if you think that stocks or equities or ETFs are the best place to put your money in 2022, you might want to think again. The crash of the OTC and small-cap market since February 2021 has been quite an indication of what a correction looks like. According to the Motley Fool what happens after major downturns in the market historically speaking? In each of the previous four instances that the S&P 500's Shiller P/E shot above and sustained 30, the index lost anywhere from 20% to 89% of its value. So what's what we too are due for, reversion to the mean will be realistically brutal after the Fed's hyper-extreme intervention has run its course. Of course what the Fed stimulus has really done is simply allowed the 1% to get a whole lot richer to the point of wealth inequality spiraling out of control in the decades ahead leading us likely to a dystopia in an unfair and unequal version of BigTech capitalism. This has also led to a trend of short squeeze to these tech stocks, as shown in recent years' data. Of course the Fed has to say that's its done all of these things for the people, employment numbers and the labor market. Women in the workplace have been set behind likely 15 years in social progress due to the pandemic and the Fed's response. While the 89% lost during the Great Depression would be virtually impossible today thanks to ongoing intervention from the Federal Reserve and Capitol Hill, a correction of 20% to 50% would be pretty fair and simply return the curve back to a normal trajectory as interest rates going back up eventually in the 2023 to 2025 period. It's very unlikely the market has taken Fed tapering into account (priced-in), since the euphoria of a can't miss market just keeps pushing the markets higher. But all good things must come to an end. Earlier this month, the U.S. Bureau of Labor Statistics released inflation data from July. This report showed that the Consumer Price Index for All Urban Consumers rose 5.2% over the past 12 months. While the Fed and economists promise us this inflation is temporary, others are not so certain. As you print so much money, the money you have is worth less and certain goods cost more. Wage gains in some industries cannot be taken back, they are permanent - in the service sector like restaurants, hospitality and travel that have been among the hardest hit. The pandemic has led to a paradigm shift in the future of work, and that too is not temporary. The Great Resignation means white collar jobs with be more WFM than ever before, with a new software revolution, different transport and energy behaviors and so forth. Climate change alone could slow down global GDP in the 21st century. How can inflation be temporary when so many trends don't appear to be temporary? Sure the price of lumber or used-cars could be temporary, but a global chip shortage is exasperating the automobile sector. The stock market isn't even behaving like it cares about anything other than the Fed, and its $billions of dollars of buying bonds each month. Some central banks will start to taper about December, 2021 (like the European). However Delta could further mutate into a variant that makes the first generation of vaccines less effective. Such a macro event could be enough to trigger the correction we've been speaking about. So stay safe, and keep your money safe. The Last Dance of the 2009 bull market could feel especially more painful because we've been spoiled for so long in the markets. We can barely remember what March, 2020 felt like. Some people sold their life savings simply due to scare tactics by the likes of Bill Ackman. His scare tactics on CNBC won him likely hundreds of millions as the stock market tanked. Hedge funds further gamed the Reddit and Gamestop movement, orchestrating them and leading the new retail investors into meme speculation and a whole bunch of other unsavory things like options trading at such scale we've never seen before. It's not just inflation and higher interest rates, it's how absurdly high valuations have become. Still correlation does not imply causation. Just because inflation has picked up, it doesn't guarantee that stocks will head lower. Nevertheless, weaker buying power associated with higher inflation can't be overlooked as a potential negative for the U.S. economy and equities. The current S&P500 10-year P/E Ratio is 38.7. This is 97% above the modern-era market average of 19.6, putting the current P/E 2.5 standard deviations above the modern-era average. This is just math, folks. History is saying the stock market is 2x its true value. So why and who would be full on the market or an asset class like crypto that is mostly speculative in nature to begin with? Study the following on a historical basis, and due your own due diligence as to the health of the markets: Debt-to-GDP ratio Call to put ratio

Tourism Revenues in Greece decreased to 3421.30 EUR Million in September from 4523.70 EUR Million in August of 2025. This dataset provides - Greece Tourism Receipts- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Key information about Nepal Tourism Revenue

• Nepal's Tourism Revenue reached 66 USD mn in Dec 2022, compared with 62 USD mn in the previous year
• Nepal's Tourism Revenue data is updated yearly, available from Dec 1990 to Dec 2022
• The data reached an all-time high of 668 USD mn in Dec 2019 and a record low of 59 USD mn in Dec 1991

The Ministry of Culture, Tourism and Civil Aviation provides annual Tourism Revenue in USD.

Tourism Revenues in Japan decreased to 31965301 JPY Thousand in September from 38640705 JPY Thousand in August of 2025. This dataset provides - Japan Tourism Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Key information about Vietnam Tourism Revenue

• Vietnam's Tourism Revenue reached 35 USD bn in Dec 2024, compared with 29 USD bn in the previous year
• Vietnam's Tourism Revenue data is updated yearly, available from Dec 2000 to Dec 2024
• The data reached an all-time high of 34,757 USD mn in Dec 2024 and a record low of 1,228 USD mn in Dec 2000

CEIC converts annual Tourism Revenue into USD. The Vietnam National Administration of Tourism provides Tourism Revenue in local currency. The State Bank of Vietnam average market exchange rate is used for currency conversions.

Introduction / Overview: HRAST is a rich, multi-label dataset with 23,113 unique user-generated review sentences designed for natural language processing tasks focused on hotel reviews. Unlike many existing datasets, it offers both sentiment labels and detailed aspect/topic annotations at the sentence level. This makes it particularly valuable for training and evaluating models in aspect-based sentiment analysis (ABSA), topic modeling, and for benchmarking. A key feature of HRAST is the inclusion of a substantial subset of sentences expressing contradicting sentiments across different aspects, presenting a significant challenge for ABSA models that process overall sentiment without isolating individual aspects. The dataset fills a critical gap in benchmark resources for the hospitality sector and is fully annotated by one human annotator and one expert annotator to ensure consistency and quality.

Context: The dataset was originally introduced by Andreou et al. (2023) to support research in aspect-based sentiment analysis and topic modeling. It was created from user-generated hotel reviews sourced from Booking.com, covering 42 hotels in four European cities: Naples, Salzburg, Barcelona, and Copenhagen. The hospitality sector was chosen due to the strong influence of user-generated reviews on consumer decision-making and hotel competitiveness.

Data Collection: The dataset was manually collected through a crowdsourcing approach by students enrolled in the Collective Intelligence course (CIS 473) at the Cyprus University of Technology. Each student was assigned a hotel listing on Booking.com and tasked with gathering 500 positive and 500 negative reviews written in English, each containing at least two sentences. Students then split the reviews into individual sentences, recorded them in Excel, and independently annotated each sentence for sentiment—positive, negative, or neutral (factual). Additionally, they labeled each sentence with one or more topics, based either on predefined Booking.com categories (such as Staff, Cleanliness, Comfort, Facilities, Location, and Value for Money) or on self-suggested topics reflecting other aspects mentioned in the reviews. In total, 16,813 reviews were collected from 42 hotels located in four European cities: Naples, Salzburg, Barcelona, and Copenhagen.

Structure and Content: Each entry represents a review sentence with a unique ID and the sentence text (review). Sentiment is labeled across three mutually exclusive columns: positive, negative, and neutral. Each sentence is also annotated for the presence of hotel-related topics, including Clean, Comfort, Facilities/Amenities, Location, Restaurant (dinner), Staff, View (Balcony), Breakfast, Room, Pool, Beach, Bathroom/Shower (toilet), Bar, Bed, Parking, Noise, Reception-checkin, Lift, Value for money, Wi-Fi, and Generic. These are binary indicators where sentences can be linked to multiple aspects simultaneously. The Aspect column signals whether the sentence contains any aspect-related content.

Usage: The dataset supports model training, validation, and benchmarking for aspect-based sentiment analysis, topic modeling, and sentiment analysis in hospitality user-generated reviews.

Citations / Credits: - Tsapatsoulis, N., Voutsa, M.C., & Djouvas, C. (2025). Biased by Design? Evaluating LLM Annotation Performance for Real-World and Synthetic Hotel Reviews. AI , forthcoming. And the original source: Andreou, C., Tsapatsoulis, N., & Anastasopoulou, V. (2023, September). A Dataset of Hotel Reviews for Aspect-Based Sentiment Analysis and Topic Modeling. In 2023 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP) 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2023) (pp. 1-9). IEEE. Licensing: CC BY-NC 4.0

In 2023, the most visited state in the United States by international tourists was New York, attracting just over nine million visitors. Florida and California followed in the ranking, with almost eight million and slightly under 6.3 million international visitors, respectively.

Tourism Revenues in Indonesia increased to 5624 USD Million in the third quarter of 2025 from 4390.10 USD Million in the second quarter of 2025. This dataset provides - Indonesia Tourism Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.