A suite of Chrome extensions developed by the Wellness Tourism Big Data Research Center for systematic collection of Google Maps, Google Play, and Apple App Store reviews built for reproducible, citation-ready research workflows.
Each extension installs in Chrome and runs locally on the researcher's machine. Output is structured (.xlsx / .csv / .json) and ready for downstream analysis in R, SPSS, or any text-mining pipeline. The Apple collector additionally uses Apple's official RSS feed for IRB-friendly data provenance.
Capture user reviews for any place listed on Google Maps. Designed for tourism, hospitality, and place-based research where review text is the primary unit of analysis.
Extract user reviews from any application on the Google Play Store. Useful for digital wellness, mHealth, and consumer-app research where longitudinal user feedback matters.
Collect reviews from the Apple App Store via Apple's official public RSS feed no scraping. Cross-country comparison across 30+ regional stores for cross-cultural research.
If you use these tools in research, please cite:
Wellness Tourism Big Data Research Center. (2026). SCTM7: Smart Crawling Text Mining (Version 7.0) [Computer software]. Kyungsung Univ. https://wtbd.site
Each tool is engineered as a step in a larger research pipeline. Install the extension, collect data within minutes, and continue directly into your preferred analytical environment.
Download the extension package, unzip, and load as an unpacked extension in Chrome's developer mode. A short installation guide is provided.
Navigate to a target Google Maps place or Play Store app, open the extension, and let it gather reviews into a structured table fully client-side.
Export to .xlsx and bring the data into R (tm, openxlsx), SPSS, or your tool of choice for text mining, sentiment, or co-occurrence analysis.
These tools emerged from ongoing research at the Wellness Tourism Big Data Research Center, where review-based text analysis informs studies in hospitality, food service, and place experience.
They are made available to fellow researchers, with the request that any publication using data collected through these tools cites the project. For partnerships or methodological inquiries, please get in touch.
A multidisciplinary group spanning hospitality management, tourism, data analytics, and applied research methodology, working across institutions in Korea and the United States.
Professor / School of Hospitality and Tourism Management, Kyungsung Univ.
Hospitality Service Management / Consumer Behavior / Tourism Big Data
Founding director of the research center. Currently serves as Professor at the School of Hospitality and Tourism Management, Kyungsung Univ., and Visiting Scholar at the Department of Nutrition & Hospitality Management, University of Mississippi.
Assistant Professor / Chairperson, School of Global Studies, Kyungsung Univ.
Service Management / Consumer Behavior / Hospitality & Tourism Management
Assistant Professor (Ph.D., University of Ulsan)
HRM / Organizational Behavior / Leadership / International Business
Assistant Professor, Kyungsung Univ.
Data Analytics for Hospitality / Big Data Analysis in Hospitality / Hospitality Management Research
Assistant Professor, Kyungsung Univ.
Data Analytics for Hospitality / Research Methodology in Hospitality
Assistant Professor, Kyungsung Univ.
Current Trends in Hospitality / Big Data Analysis in Hospitality
Assistant Professor, Kyungsung Univ.
Cost Control / Smart Foodservice Operation
Visiting Professor (Ph.D., University of Mississippi)
Smart Foodservice Operation / Tourism Event Planning
Visiting Professor (B.S., Pukyong National University)
Bar & Beverage Operation
Associate Professor, Department of Nutrition & Hospitality Management, University of Mississippi
Coordinator, Online Hospitality Management Master's Program / Coordinator, Ph.D. in Nutrition & Hospitality Management
Interim Department Chair / Associate Professor of Nutrition & Hospitality Management, University of Mississippi
Consumer Technology Adoption / Consumer Behavior / Hospitality Education
Professor, Department of Global Hospitality Management, Dong-eui University
Online Review Analysis / Digital Service Quality / Consumer Behavior
Doctoral Student, Kyungsung Univ.
Financial & Accounting Management
Doctoral Student, Kyungsung Univ.
Data Analysis / Consumer Behavior
Doctoral Student, Kyungsung Univ.
Tourism Trends / Consumer Behavior
Doctoral Student, Kyungsung Univ.
Entrepreneurship / International Student Mobility / Higher Education Internationalization
Master's Student, Kyungsung Univ.
Customer Satisfaction / Service Quality / International Student Experience
Master's Student, Kyungsung Univ.
Sport & Mega-event Tourism / Service Marketing / Media & Fan Engagement
A curated set of peer-reviewed studies from the lab in which user-generated reviews drawn from Google Maps, TripAdvisor, OTAs, and similar sources serve as the primary empirical material. These works illustrate the analytical pipelines the present tools are designed to support.
Riswanto, A. L., & Kim, H.-S. (2026). Visual attention and user satisfaction in OTA platforms: An eye-tracking and big data approach. Journal of Hospitality and Tourism Technology, Online published.
Find on Google ScholarSherpa, S., Nguyen, T. P. P., Riswanto, A. L., Kim, S., & Kim, H.-S. (2026). The gastronomic identity shift: Applying the modified health belief model and behavioral intention among international students' food adaptation in South Korea. International Journal of Tourism Research, 28(2).
doi.org/10.1002/jtr.70238Cho, H., Riswanto, A. L., Lee, G., & Kim, H.-S. (2026). Perceived value fit and customer citizenship behavior in food delivery apps' pro-environmental initiatives: An extended theory of pro-environmental planned behavior approach. International Journal of Tourism Research, 28(1), e70203.
doi.org/10.1002/jtr.70203Zhang, X., Zhong, Y., Ekaputra, R. A., & Kim, H.-S. (2026). Leveraging big data and spatial analysis for customer-centric evaluation of Chinese tourism performing arts. Journal of Hospitality and Tourism Technology, ahead-of-print.
doi.org/10.1108/JHTT-05-2025-0352Kim, S., Riswanto, A. L., Sherpa, S., & Kim, H.-S. (2025). Generation Z's Michelin choices: Survey and big data insights into Thailand's Michelin-starred restaurants. International Journal of Tourism Research, 27(5), e70135.
View on WileyKim, S., Riswanto, A. L., Williady, A., Ekaputra, R. A., & Kim, H.-S. (2025). A geospatial and big data exploration of tourism destinations: A case study of Busan, Republic of Korea. Electronic Markets, 35(1), 46.
doi.org/10.1007/s12525-025-00791-xLee, H. S. K., Wang, J., Moreno-Brito, Y. L., Shen, Y., & Kim, H.-S. (2025). Linguistic insights into customer satisfaction: An exploratory analysis of online reviews for gaming destination resorts in Las Vegas. Journal of Hospitality and Tourism Technology, 16(1), 730.
doi.org/10.1108/JHTT-05-2023-0147Wang, J., & Kim, H.-S. (2025). Exploring corporate reputation factors through cluster and sentiment analysis of CEO letters. International Journal of Information Technology & Decision Making, 24(2), 41942.
doi.org/10.1142/S0219622025500051Riswanto, A. L., Ha, Y., Kim, S., & Kim, H.-S. (2025). Visual attention to food content on social media: An eye-tracking study among young adults. Journal of Eye Movement Research, 18(6), 69.
doi.org/10.3390/jemr18060069Zhang, X., Yu, J., & Kim, H.-S. (2024). Exploring customers' experience and satisfaction with theme hotels: A cross-cultural analysis of hotel reviews. SAGE Open, 14(2), 16.
doi.org/10.1177/21582440241255827Hong, C., Choi, E. K., Joung, H. W., & Kim, H.-S. (2023). The impact of customer perceived value on customer satisfaction and loyalty toward the food delivery robot service. Journal of Hospitality and Tourism Technology, 14(5), 90824.
doi.org/10.1108/JHTT-11-2022-0305Riswanto, A. L., Kim, S., & Kim, H.-S. (2023). Analyzing online reviews to uncover customer satisfaction factors in Indian cultural tourism destinations. Behavioral Sciences, 13(11), 923.
doi.org/10.3390/bs13110923Riswanto, A. L., & Kim, H.-S. (2023). An investigation of the key attributes of Korean wellness tourism customers based on online reviews. Sustainability, 15(8), 6702.
doi.org/10.3390/su15086702Wang, J., & Kim, H.-S. (2023). Visualizing the landscape of home IoT research: A bibliometric analysis using VOSviewer. Sensors, 23(6), 3086.
doi.org/10.3390/s23063086Tao, S., & Kim, H.-S. (2022). Online customer reviews: Insights from the coffee shops industry and the moderating effect of business types. Tourism Review, 77(5), 1349364.
doi.org/10.1108/TR-12-2021-0539Pillai, S. G., Kim, W. G., Haldorai, K., & Kim, H.-S. (2022). Online food delivery services and consumers' purchase intention: Integration of theory of planned behavior, theory of perceived risk, and the elaboration likelihood model. International Journal of Hospitality Management, 105, 103275.
doi.org/10.1016/j.ijhm.2022.103275Handani, N. D., Williady, A., & Kim, H.-S. (2022). An analysis of customer textual reviews and satisfaction at luxury hotels in Singapore's Marina Bay Area (SG-Clean-Certified Hotels). Sustainability, 14(15), 9382.
doi.org/10.3390/su14159382Kim, Y.-J., & Kim, H.-S. (2022). The impact of hotel customer experience on customer satisfaction through online reviews. Sustainability, 14(2), 848.
doi.org/10.3390/su14020848Fu, W., Wei, S., Wang, J., & Kim, H.-S. (2022). Understanding the customer experience and satisfaction of casino hotels in Busan through online user-generated content. Sustainability, 14(10), 5846.
doi.org/10.3390/su14105846Zhang, X., & Kim, H.-S. (2021). Customer experience and satisfaction of Disneyland Hotel through big data analysis of online customer reviews. Sustainability, 13(22), 12699.
doi.org/10.3390/su132212699Kwon, H.-J., Ban, H.-J., Jun, J.-K., & Kim, H.-S. (2021). Topic modeling and sentiment analysis of online review for airlines. Information, 12(2), 78.
doi.org/10.3390/info12020078Ban, H.-J., & Kim, H.-S. (2020). Applying the modified health belief model (HBM) to Korean medical tourism. International Journal of Environmental Research and Public Health, 17(10), 3646.
doi.org/10.3390/ijerph17103646Shadiyar, A., Ban, H.-J., & Kim, H.-S. (2020). Extracting key drivers of air passenger's experience and satisfaction through online review analysis. Sustainability, 12(21), 9188.
doi.org/10.3390/su12219188Ban, H.-J., Choi, H., Choi, E. K., Lee, S., & Kim, H.-S. (2019). Investigating key attributes in experience and satisfaction of hotel customer using online review data. Sustainability, 11(23), 6570.
doi.org/10.3390/su11236570Ban, H.-J., & Kim, H.-S. (2019). Understanding customer experience and satisfaction through airline passengers' online review. Sustainability, 11(15), 4066.
doi.org/10.3390/su11154066Tao, S., & Kim, H.-S. (2019). Cruising in Asia: What can we dig from online cruiser reviews to understand their experience and satisfaction. Asia Pacific Journal of Tourism Research, 24(6), 51428.
doi.org/10.1080/10941665.2019.1591473Kim, H.-S., & Noh, Y. (2019). Elicitation of design factors through big data analysis of online customer reviews for washing machines. Journal of Mechanical Science and Technology, 33(6), 2785795.
doi.org/10.1007/s12206-019-0525-5Roseman, M. G., Joung, H.-W., Choi, E.-K. (C.), & Kim, H.-S. (2017). The effects of restaurant nutrition menu labelling on college students' healthy eating behaviours. Public Health Nutrition, 20(5), 79704.
doi.org/10.1017/S1368980016002871Kim, H.-S., Oh, C., & No, J. K. (2016). Can nutrition label recognition or usage affect nutrition intake according to age? Nutrition, 32(1), 560.
doi.org/10.1016/j.nut.2015.07.004Selected SSCI-indexed publications from the lab focused on user-generated review analysis and big-data approaches to hospitality and tourism. A complete list of publications is available upon request.