Yanolja Attractiveness Index
Yanolja Attractiveness Index: Methodology
The Global Tourism City Attractiveness Index, developed by Yanolja Research, is significant in that it objectively measures the attractiveness of global tourism cities from the perspective of tourists—the actual demand side of tourism.
This index directly reflects how tourists perceive and evaluate the appeal and popularity of tourism cities. It is derived through sentiment analysis and the measurement of positive and negative buzz volumes associated with specific keywords, based on social media data collected in 14 different languages. The analysis covers 191 cities worldwide, offering a comprehensive, data-driven insight into global tourist perceptions.
| Category | Description |
|---|---|
| Purpose |
Evaluation of tourism city attractiveness |
| Target cities |
191 cities (12 cities max. per country) |
| Languages analyzed |
14 languages were chosen for social media data analysis based on their large user populations and the maturity of available NLP technologies - English, Spanish, Arabic, French, Portuguese, Russian, Indonesian, German, Japanese, Turkish, Vietnamese, Korean, Italian, and Thai |
| Datasources |
News and broadcast media were excluded from data collection channels, as they predominantly contain promotional content or issue-driven reporting, which are not aligned with the objectives of this study - YouTube, Tumblr, Instagram, Blogs, Facebook Public, X(Twitter), Review, Reddit, Facebook, Forums |
| Timeframe |
Annual data aggregation over recent 2 years (2023.6 ~ 2025.5) - 2024 : 2023.6 ~ 2024.5 - 2025 : 2024.6 ~ 2025.5 - Due to technical limitations, data is only available for the most recent 2 years. Accordingly, each analysis years is operationally defined as the period from June of the previous year to May of the current year |
| Data collected & method of analysis |
Positive/negative buzz volume by keyword collected and analyzed using AI-based sentiment analysis |
| Solution used |
Brandwatch |
① Selection of Tourism Destination Cities
Tourism destination cities were selected based on the following criteria:
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① The city appears in the rankings of at least two global city indices.
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② The city is located in a country with a population of over 30 million and has a city population of at least 3 million.
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③ The city was not included based on the above criteria but was deemed necessary to include based on qualitative judgment.
A maximum of 12 cities per country is selected.
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CriteriaCriteria
Explained# of Cities
Selected -
- Global City Index
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Cities ranked in at least 2 of the 12 global city indices
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105 cities
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- Population
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Cities in countries with a population of over 30 million and city populations over 3 million
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23 cities(Duplicates with global indices excluded)
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- Committee Selection(qualitative)
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Cities not meeting the aforementioned criteria but deemed necessary based on qualitative judgment- At least 1 city per country where possible
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63 cities
② Language Selection for Analysis
For social media analysis, 14 languages were selected based on large user populations and the availability of advanced NLP technologies, making them suitable for social data analysis.
(Chinese was excluded due to government policies restricting the export of social data from within China)
| No. | Language Selected |
|---|---|
| 1 | English |
| 2 | Spanish |
| 3 | Arabic |
| 4 | French |
| 5 | Portuguese |
| 6 | Russian |
| 7 | Indonesian |
| 8 | German |
| 9 | Japanese |
| 10 | Turkish |
| 11 | Vietnamese |
| 12 | Korean |
| 13 | Italian |
| 14 | Thai |
③ Data Collection
Brandwatch, a UK-based research company, offers a comprehensive platform for social data collection, processing, analysis, and reporting. It is recognized as one of the leading solutions with the widest data coverage in the world.
In this study, Brandwatch was used to collect social media data. News and broadcast sources were excluded, as most of their content consists of promotional materials or reports on incidents and issues, which were deemed not aligned with the research objectives.
Coverage
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229 countries
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126 languages
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Over 6 million channels
Features
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Incorporates AI-based LLMs for automated semantic and sentiment classification
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Widely used by major companies both in Korea and internationally
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Utilized by the Korea Tourism Organization, indicating strong domain knowledge in the tourism sectorr
A powerful Global Social Suite powered by advanced AI models — from social data analysis to full-scale management, tailored for modern business needs.
- 1.7T+ social data coverage
- Official partner of Twitter, Reddit, Meta, Instagram and Tumblr
- Trusted by over 7,500 global clients
- Powered by Brandwatch AI Model Iris (with OpenAI GPT)
| Blogs | Forums | Reviews | |
|---|---|---|---|
| X(TWITTER) | |||
| YOUTUBE | |||
| TUMBLR |
④ Construction of Keyword Library
The selection of keywords used for social data collection followed a 4-stage process, resulting in a final set of 419 keywords utilized in the study.
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[Round 1]
Keyword SelectionInitial keyword library built per
sub-dimension based on expert input -
[Round 2]
Keyword Selection
(Pilot test)Pilot test in 5 cities resulted in top 1,000 associative keywords extracted per sub-dimension Keyword Library refined
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[Round 3]
Keyword SelectionMain survey conducted using refined keywords
Issue-prone keywords excluded during data extraction process
(e.g) “Weather” excluded due to irrelevant mentions in weather forecasts
(e.g.) “Bird” triggered noise from unrelated contents like “Angry Birds” or
“Early Bird” and was filtered using exclusion words -
[Round 4]
Keyword Selection
(Final)Keywords with excessively high or low
frequency removed for analytical validity
| Dimension | Sub-Dimension | Keywords |
|---|---|---|
| Urban Aesthetics and Natural Scenery | Natural scenery and phenomena | 30 |
| Flora and fauna | 35 | |
| Culture and History | Historical sites | 25 |
| Educational sites | 15 | |
| Traditional culture | 10 | |
| Architectural/aesthetic places | 8 | |
| Religious attractions | 35 | |
| Experiential Tourism Contents |
Food | 45 |
| Accommodations | 10 | |
| Shopping | 33 | |
| Amusement parks | 11 | |
| Nightlife | 27 | |
| Sports | 20 | |
| Activities | 30 | |
| Festivals/events | 22 | |
| Hospitality | Friendliness of local residents | 29 |
| Kindness of service providers | 34 | |
| Total | 419 | |
⑤ Modeling
This index measures tourism city attractiveness based on the positivity ratio derived through sentiment analysis of social data collected using the Keyword Library. Additionally, it measures tourism city popularity by analyzing the buzz volume (i.e., the amount of related mentions).
Tourism city attractiveness in social data is measured through a qualitative approach using sentiment analysis.
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Social media data is categorized by dimension based on keywords
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Sentiment analysis is conducted on each entry to calculate positivity ratio
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Attractiveness is measured based on the calculated positivity ratio
Buzz volume in social data reflects tourists’ voluntary mentions of a given city, indirectly indicating the destination’s visibility and accessibility.
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Social media data is categorized by dimension based on keywords
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Buzz volume is calculated for each keyword-based data entry
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Popularity of tourism cities is measured based on buzz volume
Through this study, Yanolja Research produces two key outcomes: ① the Global Tourism City Attractiveness Index, and ② the tourism city attractiveness rankings.
The Global Tourism Attractiveness Index enables observation of trends in the increase or decrease of attractiveness at both the city and global levels. Meanwhile, the tourism city attractiveness rankings help identify each tourism city’s unique strengths and its relative position within the global tourism landscape.
Attractiveness Index
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Tourism City Attractiveness Index /
Tourism City Attractiveness Index by Dimension -
Global Tourism Attractiveness Composite Index /
Global Tourism Attractiveness Composite Index by Dimension
Attractiveness Ranking
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Overall Tourism City Rankings /
Tourism City Rankings by Dimension -
Regional Tourism City Rankings /
Regional Tourism City Rankings by Dimension
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1st Tier : cities ranked 1st~50th place
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2nd Tier : cities ranked 51st ~ 100th place
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3rd Tier : cities ranked 101st ~ 150th place
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Candidate : cities ranked 151th place and below (the rankings among candidate cities will not be disclosed;
their progress will be monitored over time through continuous observation and analysis)
The Global Tourism City Attractiveness Index by Yanolja Research is measured based on the average attractiveness scores of 191 cities, ranked from 1 to 191. Each index is normalized by converting the 2024 average value to a baseline of 100, and the change in 2025 is calculated relative to this baseline. The measurement of attractiveness for each index is conducted as follows.
Attractiveness
by Sub-Dimension
Positivity Ratio × Buzz Volume Ratio
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Positivity Ratio = {Positive Buzz÷(Positive Buzz+Negative Buzz)}×100
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Buzz Volume Ratio = Total mentions for city A within a given sub-dimension
÷Total mentions across all sub-dimensions
Attractiveness by
Dimension
Attractiveness Score by Sub-Dimension × Weight per Sub-Dimension
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The weight of each Sub-Dimension is calculated based on the buzz volume of the corresponding year
Attractiveness
(Overall)
Attractiveness Score by Dimension × Weight per Dimension
| Dimension | Weight(%) |
|---|---|
| Urban Aesthetics and Natural Scenery | 30.0% |
| Culture and History | 30.0% |
| Experiential Tourism Contents | 30.0% |
| Hospitality | 10.0% |
The Global Tourism Attractiveness Composite Index is calculated by summing the overall attractiveness scores of the 191 ranked cities, normalizing the previous year’s total to a baseline of 100, and then measuring the year-over-year change relative to that baseline.