Insights from sentiment analysis to leverage local tourism business in restaurants

Yu, T., Rita, P., Moro, S., & Oliveira, C. (2021). Insights from sentiment analysis to leverage local tourism business in restaurants. International Journal of Culture, Tourism, and Hospitality Research. https://doi.org/10.1108/IJCTHR-02-2021-0037 ----------------------------------------------------------- Funding Information: The work by S. Moro and C.Oliveira was partially funded by national funds through FCT ‐ Fundação para a Ciência e Tecnologia, I.P., under the project FCT UIDB/04466/2020. The work by P. Rita was partially funded by national funds through FCT ‐ Fundação para a Ciência e Tecnologia, I.P., under the project FCT UIDB/04152/2020 ‐ Centro de Investigação em Gestão de Informação (MagIC). Publisher Copyright: © 2021, Emerald Publishing Limited.Purpose: Social media has become the main venue for users to express their opinions and feelings, generating a vast number of available and valuable data to be scrutinized by researchers and marketers. This paper aims to extend previous studies analyzing social media reviews through text mining and sentiment analysis to provide useful recommendations for management in the restaurant industry. Design/methodology/approach: The Lexalytics, a text mining artificial intelligence tool, is applied to analyze the text of the online reviews of the restaurants in a touristic Dutch village extracted from the most frequently used social media platforms focusing on the four restaurant quality factors, namely, food and beverage, service, atmosphere and value. Findings: The findings of this research are presented by the identified key themes with comparisons of the customers’ review sentiment between a selected restaurant, Zwaantje, vis-à-vis its bench-mark restaurants set by a specific approach under the abovementioned quality dimensions, in which the food and beverage and service are the most commented by customers. Results demonstrate that text mining can generate insights from different aspects and that the proposed approach is valuable to restaurant management. Originality/value: The paper provides a relatively big scale in numbers and resources of social media reviews to further explore the most important service dimensions in the restaurant industry in a specific tourist area. It also offers a useful framework to apply the text mining business intelligence tool by comparison of peers for local small business restaurant practitioners to improve their management skills beyond manually reading social media reviews.authorsversionepub_ahead_of_prin

BITOUR: A Business Intelligence Platform for Tourism Analysis

[EN] Integrating collaborative data in data-driven Business Intelligence (BI) system brings an opportunity to foster the decision-making process towards improving tourism competitiveness. This article presents BITOUR, a BI platform that integrates four collaborative data sources (Twitter, Openstreetmap, Tripadvisor and Airbnb). BITOUR follows a classical BI architecture and provides functionalities for data transformation, data processing, data analysis and data visualization. At the core of the data processing, BITOUR offers mechanisms to identify tourists in Twitter, assign tweets to attractions and accommodation sites from Tripadvisor and Airbnb, analyze sentiments in opinions issued by tourists, and all this using geolocation objects in Openstreetmap. With all these ingredients, BITOUR enables data analysis and visualization to answer questions like the most frequented places by tourists, the average stay length or the view of visitors of some particular destination.This work has been supported by COLCIENCIAS through a PhD scholarship. This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R.Bustamante, A.; Sebastiá Tarín, L.; Onaindia De La Rivaherrera, E. (2020). BITOUR: A Business Intelligence Platform for Tourism Analysis. ISPRS International Journal of Geo-Information. 9(11):1-23. https://doi.org/10.3390/ijgi9110671S123911Nakahira, K. T., Akahane, M., & Fukami, Y. (2012). The Difference and Limitation of Cognition for Piano Playing Skill with Difference Educational Design. Smart Innovation, Systems and Technologies, 609-617. doi:10.1007/978-3-642-29934-6_59Chua, A., Servillo, L., Marcheggiani, E., & Moere, A. V. (2016). Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy. Tourism Management, 57, 295-310. doi:10.1016/j.tourman.2016.06.013Karagiannakis, N., Giannopoulos, G., Skoutas, D., & Athanasiou, S. (2015). OSMRec Tool for Automatic Recommendation of Categories on Spatial Entities in OpenStreetMap. Proceedings of the 9th ACM Conference on Recommender Systems. doi:10.1145/2792838.2796555Burcher, M., & Whelan, C. (2017). Social network analysis as a tool for criminal intelligence: understanding its potential from the perspectives of intelligence analysts. Trends in Organized Crime, 21(3), 278-294. doi:10.1007/s12117-017-9313-8Alcabnani, S., Oubezza, M., & Elkafi, J. (2019). An approach for the implementation of semantic Big Data Analytics in the Social Business Intelligence process on distributed environments (Cloud computing). Proceedings of the 4th International Conference on Big Data and Internet of Things. doi:10.1145/3372938.3373003Zeng, B., & Gerritsen, R. (2014). What do we know about social media in tourism? A review. Tourism Management Perspectives, 10, 27-36. doi:10.1016/j.tmp.2014.01.001Lalicic, L. (2018). Open innovation platforms in tourism: how do stakeholders engage and reach consensus? International Journal of Contemporary Hospitality Management, 30(6), 2517-2536. doi:10.1108/ijchm-04-2016-0233Dwyer, L., & Kim, C. (2003). Destination Competitiveness: Determinants and Indicators. Current Issues in Tourism, 6(5), 369-414. doi:10.1080/13683500308667962Gomezelj, D. O., & Mihalič, T. (2008). Destination competitiveness—Applying different models, the case of Slovenia. Tourism Management, 29(2), 294-307. doi:10.1016/j.tourman.2007.03.009Zhong, L., Deng, J., & Xiang, B. (2008). Tourism development and the tourism area life-cycle model: A case study of Zhangjiajie National Forest Park, China. Tourism Management, 29(5), 841-856. doi:10.1016/j.tourman.2007.10.002Fernández, J. I. P., & Rivero, M. S. (2009). Measuring Tourism Sustainability: Proposal for a Composite Index. Tourism Economics, 15(2), 277-296. doi:10.5367/000000009788254377Cibinskiene, A., & Snieskiene, G. (2015). Evaluation of City Tourism Competitiveness. Procedia - Social and Behavioral Sciences, 213, 105-110. doi:10.1016/j.sbspro.2015.11.411Business Intelligence (BI)—Glossaryhttps://www.gartner.com/it-glossary/business-intelligence-bi/Mariani, M., Baggio, R., Fuchs, M., & Höepken, W. (2018). Business intelligence and big data in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, 30(12), 3514-3554. doi:10.1108/ijchm-07-2017-0461Maeda, T. N., Yoshida, M., Toriumi, F., & Ohashi, H. (2016). Decision Tree Analysis of Tourists’ Preferences Regarding Tourist Attractions Using Geotag Data from Social Media. Proceedings of the Second International Conference on IoT in Urban Space. doi:10.1145/2962735.2962745Guy, I., Mejer, A., Nus, A., & Raiber, F. (2017). Extracting and Ranking Travel Tips from User-Generated Reviews. Proceedings of the 26th International Conference on World Wide Web. doi:10.1145/3038912.3052632Peng, M. Y.-P., Tuan, S.-H., & Liu, F.-C. (2017). Establishment of Business Intelligence and Big Data Analysis for Higher Education. Proceedings of the International Conference on Business and Information Management - ICBIM 2017. doi:10.1145/3134271.3134296Castellanos, M., Gupta, C., Wang, S., Dayal, U., & Durazo, M. (2012). A platform for situational awareness in operational BI. Decision Support Systems, 52(4), 869-883. doi:10.1016/j.dss.2011.11.011Cohen, L. (2017). Impacts of business intelligence on population health. Proceedings of the South African Institute of Computer Scientists and Information Technologists on - SAICSIT ’17. doi:10.1145/3129416.3129441Love, M., Boisvert, C., Uruchurtu, E., & Ibbotson, I. (2016). Nifty with Data. Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education. doi:10.1145/2899415.2899431Berndt, D. J., Hevner, A. R., & Studnicki, J. (s. f.). Hospital discharge transactions: a data warehouse component. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences. doi:10.1109/hicss.2000.926791Musa, G. J., Chiang, P.-H., Sylk, T., Bavley, R., Keating, W., Lakew, B., … Hoven, C. W. (2013). Use of GIS Mapping as a Public Health Tool–-From Cholera to Cancer. Health Services Insights, 6, HSI.S10471. doi:10.4137/hsi.s10471Mooney, S. J., Westreich, D. J., & El-Sayed, A. M. (2015). Commentary. Epidemiology, 26(3), 390-394. doi:10.1097/ede.0000000000000274Wisniewski, M. F., Kieszkowski, P., Zagorski, B. M., Trick, W. E., Sommers, M., & Weinstein, R. A. (2003). Development of a Clinical Data Warehouse for Hospital Infection Control. Journal of the American Medical Informatics Association, 10(5), 454-462. doi:10.1197/jamia.m1299Miah, S. J., Vu, H. Q., Gammack, J., & McGrath, M. (2017). A Big Data Analytics Method for Tourist Behaviour Analysis. Information & Management, 54(6), 771-785. doi:10.1016/j.im.2016.11.011Li, D., Deng, L., & Cai, Z. (2019). Statistical analysis of tourist flow in tourist spots based on big data platform and DA-HKRVM algorithms. Personal and Ubiquitous Computing, 24(1), 87-101. doi:10.1007/s00779-019-01341-xKrawczyk, M., & Xiang, Z. (2015). Perceptual mapping of hotel brands using online reviews: a text analytics approach. Information Technology & Tourism, 16(1), 23-43. doi:10.1007/s40558-015-0033-0Alaei, A. R., Becken, S., & Stantic, B. (2017). Sentiment Analysis in Tourism: Capitalizing on Big Data. Journal of Travel Research, 58(2), 175-191. doi:10.1177/0047287517747753Thelwall, M. (2019). Sentiment Analysis for Tourism. Big Data and Innovation in Tourism, Travel, and Hospitality, 87-104. doi:10.1007/978-981-13-6339-9_6Höpken, W., Fuchs, M., Höll, G., Keil, D., & Lexhagen, M. (2013). Multi-Dimensional Data Modelling for a Tourism Destination Data Warehouse. Information and Communication Technologies in Tourism 2013, 157-169. doi:10.1007/978-3-642-36309-2_14Sabou, M., Onder, I., Brasoveanu, A. M. P., & Scharl, A. (2015). Towards Cross-Domain Decision Making in Tourism: A Linked Data Based Approach. SSRN Electronic Journal. doi:10.2139/ssrn.2580242Fermoso, A. M., Mateos, M., Beato, M. E., & Berjón, R. (2015). Open linked data and mobile devices as e-tourism tools. A practical approach to collaborative e-learning. Computers in Human Behavior, 51, 618-626. doi:10.1016/j.chb.2015.02.032Wöber, K. W. (2003). Information supply in tourism management by marketing decision support systems. Tourism Management, 24(3), 241-255. doi:10.1016/s0261-5177(02)00071-7Vajirakachorn, T., & Chongwatpol, J. (2017). Application of business intelligence in the tourism industry: A case study of a local food festival in Thailand. Tourism Management Perspectives, 23, 75-86. doi:10.1016/j.tmp.2017.05.003Diakopoulos, N., Naaman, M., & Kivran-Swaine, F. (2010). Diamonds in the rough: Social media visual analytics for journalistic inquiry. 2010 IEEE Symposium on Visual Analytics Science and Technology. doi:10.1109/vast.2010.5652922Bustamante, A., Sebastia, L., & Onaindia, E. (2019). Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks. Sensors, 19(11), 2612. doi:10.3390/s19112612Yasseri, T., Quattrone, G., & Mashhadi, A. (2013). Temporal analysis of activity patterns of editors in collaborative mapping project of OpenStreetMap. Proceedings of the 9th International Symposium on Open Collaboration. doi:10.1145/2491055.2491068Jilani, M., Corcoran, P., & Bertolotto, M. (2013). Multi-granular Street Network Representation towards Quality Assessment of OpenStreetMap Data. Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science - IWCTS ’13. doi:10.1145/2533828.2533833Luxen, D., & Vetter, C. (2011). Real-time routing with OpenStreetMap data. Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS ’11. doi:10.1145/2093973.2094062Baumbach, S., Rubel, C., Ahmed, S., & Dengel, A. (2019). Geospatial Customer, Competitor and Supplier Analysis for Site Selection of Supermarkets. Proceedings of the 2019 2nd International Conference on Geoinformatics and Data Analysis. doi:10.1145/3318236.3318264Milot, J., Munroe, P., Beaudry, E., Grondin, F., & Bourdeau, G. (2016). Lookupia. Proceedings of the 25th International Conference Companion on World Wide Web - WWW ’16 Companion. doi:10.1145/2872518.2890485Ciepluch, B., Mooney, P., Jacob, R., & Winstanley, A. C. (2009). Using OpenStreetMap to deliver location-based environmental information in Ireland. SIGSPATIAL Special, 1(3), 17-22. doi:10.1145/1645424.1645428Del Pilar Salas-Zárate, M., López-López, E., Valencia-García, R., Aussenac-Gilles, N., Almela, Á., & Alor-Hernández, G. (2014). A study on LIWC categories for opinion mining in Spanish reviews. Journal of Information Science, 40(6), 749-760. doi:10.1177/0165551514547842Gambino, O. J., & Calvo, H. (2016). A Comparison Between Two Spanish Sentiment Lexicons in the Twitter Sentiment Analysis Task. Advances in Artificial Intelligence - IBERAMIA 2016, 127-138. doi:10.1007/978-3-319-47955-2_11Mooney, P., Corcoran, P., & Winstanley, A. C. (2010). Towards quality metrics for OpenStreetMap. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS ’10. doi:10.1145/1869790.1869875El-Ashmawy, K. l. A. (2016). TESTING THE POSITIONAL ACCURACY OF OPENSTREETMAP DATA FOR MAPPING APPLICATIONS. Geodesy and cartography, 42(1), 25-30. doi:10.3846/20296991.2015.116049

The necessities for building a model to evaluate Business Intelligence projects- Literature Review

In recent years Business Intelligence (BI) systems have consistently been rated as one of the highest priorities of Information Systems (IS) and business leaders. BI allows firms to apply information for supporting their processes and decisions by combining its capabilities in both of organizational and technical issues. Many of companies are being spent a significant portion of its IT budgets on business intelligence and related technology. Evaluation of BI readiness is vital because it serves two important goals. First, it shows gaps areas where company is not ready to proceed with its BI efforts. By identifying BI readiness gaps, we can avoid wasting time and resources. Second, the evaluation guides us what we need to close the gaps and implement BI with a high probability of success. This paper proposes to present an overview of BI and necessities for evaluation of readiness. Key words: Business intelligence, Evaluation, Success, ReadinessComment: International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 201

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes