Tourism Reputation Index for Assessing Perceptions on Destinations Using Collaborative Text Data

  1. Paula Antón Maraña 1
  2. Bruno Baruque Zanón 2
  3. Santiago Porras Alfonso 1
  4. Pablo Arranz Val 1
  1. 1 Department of Applied Economics, UNIVERSITY OF BURGOS, SPAIN
  2. 2 Department of Computer Engineering, UNIVERSITY OF BURGOS, SPAIN
Revista:
Estudios de economía aplicada

ISSN: 1133-3197 1697-5731

Año de publicación: 2023

Título del ejemplar: Ciencia de datos para la Economía Aplicada

Volumen: 41

Número: 1

Tipo: Artículo

DOI: 10.25115/SAE.V41I1.9076 DIALNET GOOGLE SCHOLAR

Otras publicaciones en: Estudios de economía aplicada

Objetivos de desarrollo sostenible

Resumen

El sector turístico está experimentando un cambio de tendencia ante el uso generalizado de la Web 2.0. por parte de los turistas. Este fenómeno ha impulsado a los agentes turísticos a aplicar técnicas Big Data y herramientas de Inteligencia de Negocio a la hora de conocer qué piensan y qué desean a los turistas. En este sentido, toma especial relevancia el análisis del Electronic Word Of Mouth en las Redes Sociales, y la literatura académica ha encontrado algunas dificultades al extraer datos, determinar su significado y vincularlo con el destino. El objetivo de este trabajo es presentar los resultados que se pueden obtener con el diseño de un índice reputacional turístico, por medio del análisis de los datos creados de manera colaborativa en la Red Social de Twitter. Para ello, se ha creado una herramienta de IN que permite extraer datos de los mensajes cortos de texto publicados por los usuarios (tweets) y procesarlos mediante la implementación de un proceso de Extract, Transform and Load. En este proceso, se realiza un Procesamiento del Lenguaje Natural, centrado en tareas de Análisis de Sentimientos, aplicando un modelo de clasificación automática (Multinomial Naive Bayes) y, seguidamente, una categorización temática automática de estos textos en función de los términos que contengan. Posteriormente, se analizan los datos, calculando un índice de reputación online global a partir de subíndices de percepción sobre diferentes aspectos turísticos. Estos índices se muestran a los usuarios mediante un cuadro de mando interactivo, que permite comparar entre diferentes consultas asociadas a una localidad turística y a un periodo temporal. Este trabajo contribuye a que los agentes turísticos conozcan la percepción sobre los destinos y tomen decisiones adecuadas e informadas en tiempo real, facilitando una gestión inteligente de los datos que permita crear destinos turísticos inteligentes con ventajas competitivas, únicas y sostenibles.

Referencias bibliográficas

  • Giaoutzi, M. and Nijkamp, P. (2017). Tourism and Regional Development: New Pathways. In Tourism and Regional Development: New Pathways. Taylor and Francis.
  • Osman, Z. and Sentosa, I. (2013). A study of mediating effect of trust on customer satisfaction and customer loyalty relationship in Malaysian rural tourism. European Journal of Tourism Research, 6(2), 192–206.
  • Díaz, A. (July 10, 2021). Aportación directa del turismo al PIB en el mundo 2013-2019. https://es.statista.com/estadisticas/640133/aportacion-directa-del-sector-turistico-al-pib-mundial/
  • INE. (July 11, 2021). Cuenta Satélite del Turismo de España (CSTE). Revisión estadística 2019. Serie 2016 – 2018.
  • Cook, R. A., Yale, L. J., & Marqua, J. J. (2010). Tourism: The Business of Travel (Pearson (ed.); 4Th ed.).
  • Ardito, L., Cerchione, R., Del Vecchio, P. and Raguseo, E. (2019). Big data in smart tourism: challenges, issues and opportunities. Current Issues in Tourism, 22(15), 1805–1809.
  • Gobierno de España. (June 16, 2021). Plan de modernización y competitividad del sector turístico.
  • Observatorio eCommerce & Transformación DIgital. (July 20, 2021). Impacto de internet en el sector turismo. https://observatorioecommerce.com/impacto-internet-turismo/
  • Bustamante-Martínez, A. A. (2020). Análisis de datos colaborativos e inteligencia de negocio: aplicación al sector turístico. In Tesis de doctorado. Universitat Politècnica de València.
  • Romero-Dexeus, C. and Paton, J. (15 June 2021). Innovación Turística y Especialización Inteligente en España. Palancas Imprescindibles para la Recuperación. https://www.segittur.es/wp-content/uploads/2021/03/Informe-Innovacion-Turistica-y-Especializacion-Inteligente-en-Espana.pdf
  • Castellanos, M., Gupta, C., Wang, S., Dayal, U. and Durazo, M. (2012). A platform for situational awareness in operational BI. Decision Support Systems, 52(4), 869–883.
  • Peng, M. Y. P., Tuan, S. H. and Liu, F. C. (2017). Establishment of business intelligence and big data analysis for higher education. International Conference on Business and Information Management, Part F131932, 121–125.
  • Mariani, M., Baggio, R., Fuchs, M. and 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.
  • Li, D., Deng, L. and Cai, Z. (2019). Statistical analysis of tourist flow in tourist spots based on big data platform and DA-HKRVM algorithms. Personal and Ubiquitous Computing 2019, 24(1), 87–101.
  • Miah, S. J., Vu, H. Q., Gammack, J. and McGrath, M. (2017). A Big Data Analytics Method for Tourist Behaviour Analysis. Information & Management, 54(6), 771–785.
  • Talón-Ballestero, P., González-Serrano, L., Soguero-Ruiz, C., Muñoz-Romero, S. and Rojo-Álvarez, J. L. (2018). Using big data from Customer Relationship Management information systems to determine the client profile in the hotel sector. Tourism Management, 68, 187–197.
  • Song, H., Witt, S. F., Wong, K. F. and Wu, D. C. (2009). An empirical study of forecast combination in tourism. Journal of Hospitality and Tourism Research, 33(1), 3–29.
  • Sun, S., Wei, Y., Tsui, K. L. and Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1–10.
  • Centobelli, P. and Ndou, V. (2019). Managing customer knowledge through the use of big data analytics in tourism research. Current Issues in Tourism, 22(15), 1862–1882.
  • Thelwall, M. (2019). Sentiment Analysis for Tourism. In Big Data and Innovation in Tourism, Travel, and Hospitality: Managerial Approaches, Techniques, and Applications. Springer.
  • Fermoso, 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.
  • Giglio, S., Bertacchini, F., Bilotta, E. and Pantano, P. (2019). Using social media to identify tourism attractiveness in six Italian cities. Tourism Management, 72, 306–312.
  • Hui, T. K., Wan, D. and Ho, A. (2007). Tourists’ satisfaction, recommendation and revisiting Singapore. Tourism Management, 28(4), 965–975.
  • Chen, K. C. (2004). Decision support system for tourism development: System dynamics approach. Journal of Computer Information Systems, 45(1), 104–112.
  • Bismart. (July 20, 2021). How Big Data Technologies Can Improve Tourism. https://blog.bismart.com/en/big-data-technologies-tourism
  • Business Analytics and Bigdata. (20 de julio de 2021). Optimizadata. https://optimizadata.com/
  • Mabrian. (July 5, 2021). Plataforma de Inteligencia Turística Mabrian. https://mabrian.com/solutions/destinations/
  • Sabou, M., Brașoveanu, A. M. P., & Önder, I. (2015). Linked Data for Cross-Domain Decision-Making in Tourism. Information and Communication Technologies in Tourism 2015, 197–210.
  • Segittur. (July 5, 2021). Catálogo de soluciones tecnológicas para Destinos Turísticos Inteligentes. https://www.segittur.es/wp-content/uploads/2021/05/GUIA-SOLUCIONES-TECNOLOGICAS-DTI-2021.pdf
  • Vajirakachorn, T. and 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.
  • Wöber, K. W. (2003). Information supply in tourism management by marketing decision support systems. Tourism Management, 24(3), 241–255.
  • De Vries, L., Gensler, S. and Leeflang, P. S. H. (2012). Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing. Journal of Interactive Marketing, 26(2), 83–91.
  • Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18(1), 38–52.
  • Martín-Galán, C. A. (2020). Diseño e implementación de un Sistema Automático para la detección de opiniones turísticas en Redes Sociales. Universidad de La Laguna.
  • Leung, D., Law, R., Hoof, H. van, & Buhalis, D. (2013). Social Media in Tourism and Hospitality: A Literature Review. Journal of Travel & Tourism Marketing, 30(1–2), 3–22.
  • Munar, A. M. and Jacobsen, J. K. S. (2014). Motivations for sharing tourism experiences through social media. Tourism Management, 43, 46–54.
  • Murthy, D. (2013). Twitter: Social Communication in the Twitter Age. Cambridge.
  • Maeda, T. N., Yoshida, M., Toriumi, F. and Ohashi, H. (2016). Decision tree analysis of tourists’ preferences regarding tourist attractions using geotag data from social media. International Conference on IoT in Urban Space, 24-25-May-2016, 61–64.
  • Bassolas, A., Lenormand, M., Tugores, A., Gonçalves, B. and Ramasco, J. J. (2016). Touristic site attractiveness seen through Twitter. EPJ Data Science, 5(1).
  • Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P. and Ratti, C. (2014). Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260–271.
  • Bustamante, A., Sebastia, L. and Onaindia, E. (2019). Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks. Sensors 2019, 19(11).
  • Diakopoulos, N., Naaman, M., & Kivran-Swaine, F. (2010). Diamonds in the rough: Social media visual analytics for journalistic inquiry. IEEE Conference on Visual Analytics Science and Technology, 115–122.
  • Hays, S., Page, S. J., & Buhalis, D. (2013). Social media as a destination marketing tool: its use by national tourism organisations. Current Issues in Tourism, 16(3), 211–239.
  • Milano, R., Baggio, R. and Piattelli, R. (2011). The effects of online social media on tourism websites. Information and Communication Technologies in Tourism 2011, 471–483.
  • Perles-Ribes, J. F., Ramón-Rodríguez, A. B., Moreno-Izquierdo, L. and Such-Devesa, A. M. J. (2019). Online Reputation and Destination Competitiveness: The Case of Spain. Tourism Analysis, 24, 161–176.
  • Ortiz, Á. L., Pérez la Madrid, Ó. E. and Vargas Rastrollo, E. (2015). Estudio de Tendencias Diarias en Twitter. Trabajo de Fin de Grado.
  • Stojanovski, D., Dimitrovski, I. and Madjarov, G. (2014). TweetViz: Twitter Data Visualization. Conference on Data Mining and Data Warehouses (SiKDD 2014) - Information Society.
  • Strapparava, C. and Valitutti, A. (2004). WordNet Affect: an Affective Extension of WordNet. Proceedings of LREC, 1083–1086.
  • Bradley, M. M. and Lang, P. J. (1999). Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings. In University of Florida: The Center for Research in Psychophysiology.
  • Stanford NLP Group. (July 25, 2021). Available Models & Languages - Stanza. https://stanfordnlp.github.io/stanza/available_models.html
  • Sidorov, G., Miranda-Jiménez, S., Viveros-Jiménez, F., Gelbukh, A., Castro-Sánchez, N., Velásquez, F., Díaz-Rangel, I., Suárez-Guerra, S., Treviño, A., & Gordon, J. (2012). Empirical Study of Machine Learning Based Approach for Opinion Mining in Tweets. In I. Batyrshin & M. González Mendoza (Eds.), Advances in Artificial Intelligence (7629 1, pp. 1–14). Springer.
  • Acevedo-Miranda, C., Clorio-Rodríguez, R., Zagal-Flores, R. and García-Mendoza, C. V. (2014). Arquitectura Web para análisis de sentimientos en Facebook con enfoque semántico. Research in Computing Science, 75, 59–69.
  • Aguilar, D., Sidorov, G. and Batyrshin, I. (2018). Caso de estudio de análisis de sentimientos en Twitter: Tratado de libre comercio de América del Norte. Research in Computing Science, 147(5), 357–365.
  • Gambino, O. J. and Calvo, H. (2016). A Comparison Between Two Spanish Sentiment Lexicons in the Twitter Sentiment Analysis Task. In Lecture Notes in Computer Science (10022, pp. 127–138). Springer, Cham.
  • Salas-Zárate, M. del P., López-López, E., Valencia-García, R., Aussenac-Gilles, N., Almela, Á. and Alor-Hernández, G. (2014). A study on LIWC categories for opinion mining in Spanish reviews. Journal of Information Science, 40(6), 749–760.
  • Darwish, A. and Burns, P. (2019). Tourist destination reputation: an empirical definition. Tourism Recreation Research, 44(2), 153-162.
  • Marchiori, E., Cantoni, L. and Fesenmaier, D. R. (2013). What did they say about us? Message Cues and Destination Reputation in Social Media. In L. Cantoni & Z. Xiand (Eds.), Information and Communication Technologies in Tourism (pp. 170–182). Springer.
  • Cankir, B., Arslan, M. L. and Seker, S. E. (2015). Web Reputation Index for XU030 Quote Companies. Journal of Industrial and Intelligent Information, 3(2).
  • Vaccaro, G., Cabrera, F. E., Pelaez, J. I. and Vargas, L. G. (2020). Comparison matrix geometric index: A qualitative online reputation metric. Applied Soft Computing, 96, 106687.
  • SocialVane. (July 16, 2021). Ranking Big Data de Costas Españolas. http://docplayer.es/24213366-Estudio-de-costas.html
  • Prayag, G. (2011). Image, Satisfaction and Loyalty—The Case of Cape Town. An International Journal of Tourism and Hospitality Research, 19(2), 205–224.
  • Rajesh, R. (2013). Impact of Tourist Perceptions, Destination Image and Tourist Satisfaction on Destination Loyalty: A Conceptual Model. PASOS. Revista de Turismo y Patrimonio Cultural, 11(3), 67–78.
  • Denstadli, J. M., Jacobsen, J. K. S., and Lohmann, M. (2011). Tourist perceptions of summer weather in Scandinavia. Annals of Tourism Research, 38(3), 920–940.
  • Prayag, G. and Ryan, C. (2012). Antecedents of Tourists’ Loyalty to Mauritius: The Role and Influence of Destination Image, Place Attachment, Personal Involvement, and Satisfaction. Journal of Travel Research, 51(3), 342–356.
  • Martín-Sánchez, J. M., Delgado Martín, L. M. and Gallego Rengifo, J. I. (2019). La reputación online de los alojamientos rurales en Extremadura desde una óptica geoestadística. Boletín de La Asociación de Geógrafos Españoles, 82(2758), 1–36.
  • Iglesias-Sánchez, P. P., Correia, M. B. and Jambrino-Maldonado, C. (2019). Challenges in linking destinations’ online reputation with competitiveness. Tourism & Management Studies, 15(1), 35–43.
  • Reichheld, F. F. (2003). The one number you need to grow. The One Number You Need to Grow, 81(12), 46–55.
  • Kushcheva, N. (2022). Monitoring Online Reputation of Tourist Destinations in Finland. INTED2022 Proceedings, 9442–9447.
  • Komšić, J., & Dorčić, J. (2016). Tourism Destination Competitiveness and Online Reputation: Conceptualization and Literature Framework Analysis. Tourism & Hospitality Industry, 144–157.