Destination image analytics through traveller-generated content

The explosion of content generated by users, in parallel with the spectacular growth of social media and the proliferation of mobile devices, is causing a paradigm shift in research. Surveys or interviews are no longer necessary to obtain users' opinions, because researchers can get this information freely on social media. In the field of tourism, online travel reviews (OTRs) hosted on travel-related websites stand out. The objective of this article is to demonstrate the usefulness of OTRs to analyse the image of a tourist destination. For this, a theoretical and methodological framework is defined, as well as metrics that allow for measuring different aspects (designative, appraisive and prescriptive) of the tourist image. The model is applied to the region of Attica (Greece) through a random sample of 300,000 TripAdvisor OTRs about attractions, activities, restaurants and hotels written in English between 2013 and 2018. The results show trends, preferences, assessments, and opinions from the demand side, which can be useful for destination managers in optimising the distribution of available resources and promoting sustainability

A Review of Text Corpus-Based Tourism Big Data Mining

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years

A Review of Text Corpus-Based Tourism Big Data Mining

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years

The Impact of Information and Communication Technology on the Tourism Sector

Information and Communication Technology (ICT) has changed the global businesses environment by a wide range of tools, methodologies and functions, facilitating the strategic management and supporting firms to achieve a long term competitive advantage. The aim of this paper is to provide an overview of the new applications of Information Communication Technology in tourism industry, the contribution of ICT to the promotion of the tourist product, as well as the potential to the tourism management and the process of decision-making. One important tool, which helps in making decisions in the field of tourism economy, is the Geographic Information System (GIS), which provides a comprehensible representation of the statistical figures of the tourism economy by facilitating decision-making on tourism policy. In this paper is presented some tourist financial figures and their visualization through graphs by Geographic Information System

BITOUR: A Business Intelligence Platform for Tourism Analysis

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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). 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Lake-destination image attributes: A neural network content analysis

This paper aims to explore and analyse lake image attributes extracted from a content analysis of an online directory for lake enthusiasts. CATPAC, a text-mining software program based on artificial neural networks, was adopted. The resulting output was used to identify the words that were most frequently mentioned to portray image attributes related to lake-destination areas (LDA). The findings also revealed that the final set of LDA image attributes is intertwined with the main dimensions of the lake tourism concept. Keywords: Destination image, lake tourism, lake-destination areas, image attributes, content-analysis, CATPAC

MARKETING COMMUNICATION IN THE VISIT PHASE THROUGH GUEST.NET - AN INDESTINATION, LOCATION-BASED SYSTEM AT MAISTRA HOTEL CHAIN IN CROATIA

Hoteliers have always endeavoured to retain guests within their facilities, to profit maximally by offering them additional services. Informing guests of various options is performed within in-house marketing techniques, whereas some are ICT based. Purpose. Hotel chain websites are aimed at the acquisition of guests and as such are inadequate for displaying detailed, service and current information that guests need during their stay in a tourist destination (e.g. happy hour offer at the lobby bar). What about information provided to guests once in the tourist destination? This paper will present one such solution: the Guest.net. It is an in-destination, location-based website accessible from all Maistra Inc. properties, representing a good solution for hotel chains with various nearby positioned tourism facilities aimed at retaining guests within chain facilities. Design/Methodology/Approach. The approach used in this paper is the case study method. Findings and Implications. Klante, Kroschel and Bolls theoretical information model (2004) was expanded by adding the planning in situ phase in tourism. Benefits of the application of similar guest services for hotel chains have been listed. Limitations. Limitations steam from the case study method and relate to the minor geographical area researched. Originality. There is an evident lack in research of customers in tourism during the visit phase, regarding their decision making process, especially in the evaluation of alternatives and their purchase decision in situ (Law, Buhalis, Cobanoglu, 2014), thus this paper broadens the identified gap in the information collection phase in the destination

Exploring the benefits of using a mixed methods approach in destination image studies

This study aims to demonstrate that mixed-methods are suitable when assessing the image of a tourism destination. Depict the image attributes that influence a lake destination area and conceptualize lake tourism are the goals of this study. Lake tourism is a growing academic field of tourism studies. However, little attention has been given to tourism images. The case is the newly-formed Alqueva Lake, Portugal, the biggest man-made lake in Europe. A mixed-design method was adopted, particularly a complementarity approach. The data were first collected in the qualitative stage, then analysed. Results were used to develop a follow-up questionnaire. A set of image attributes that best describe Alqueva Lake was obtained and validated. The advantages of adopting mixed-methods to studies about destination image are discussed

Tourist Responses to Tourism Experiences in Saudi Arabia

A decade ago, the Kingdom of Saudi Arabia (KSA) was not perceived to be a popular tourism destination except for religious purposes, the government of KSA has been proactive in recent years building new destinations, changing longstanding policies, focusing on tourism and hospitality education, and renovating its image to attract domestic and international tourists. Tourism contributed to almost 9% of the Kingdom’s GDP in 2018, around 65 billion dollars (WTTC, 2019). The purpose of this paper is to understand the sentiment that tourists have regarding the new tourism campaigns in KSA, to have transparent feedback about the experiences and services mostly adopted by tourists, and to study the feasibility of KSA Vision 2030 regarding the tourism sector. This study will perform an open data analysis by extracting and analyzing data from a well-known online source (Twitter). Results will highlight the utilization of online data tools to measure tourism trends

How to monitor and generate intelligence for a DMO from online reviews

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing IntelligenceSocial media and customer review websites have changed the way the tourism sector is managed. Social media has become a new source of information, due to the large amount of UGC / e-Wom generated by consumers An information that is "available" but at the same time noisy and of great volume, which makes it difficult to access and analyze. This study investigates and verifies the possibility of using data present in content reviews of a Content Web Site Review - TripAdivsor - to generate actionable information for a Destination Management Organization. With a focus on negative reviews, tourist attractions of Lisbon and using the “R code” and its packages, the study shows that with the correct technique chosen and the action of an intelligence analyst, data can be extracted and provide substrate for actions, strategy and intelligence generation – which is Social Media Intelligence. The findings prove that the flood of web 2.0 data can serve as a source of intelligence for the Destination Management Organization (DMO). By monitoring sites like TripAdvisor, a DMO can hear what tourists talk about attractions and thereby generate insights for intelligence and strategy actions. A DMO can even, analyzing this data, make your attractions more desirable, and even act in adverse situations, reducing risky situations