PROFILING SOCIAL MEDIA TOURISTS USING LITERATURE DURING 2015-2019: CRIMINAL PROFILING METHOD

With the continuous development of mobile commerce and the Internet, social media has deeply penetrated people’s lives and fundamentally changed the way of searching, reading and using travel-related information. With this backdrop, this research studied social media tourists (SMTs) who share or acquire information related to the hospitality and tourism on social media platforms. Based on 271 empirical articles retrieved from major databases and top hospitality and tourism journals in the recent five years from 2015 to 2019, this research developed a profiling framework about SMTs using criminal profiling method. The findings showed the possibility of using the criminal profiling method to analyze SMTs and provided a holistic personal, social-psychological, and behavioral profile of SMTs. Theoretical and practical implications were discussed

Assessing destination image: an online marketing approach and the case of Tripadvisor

Destination image is a popular research domain in the tourism literature. Yet, limited studies focus on destination image as reflected through actual tourists’ evaluations and reviews on social media. Taken the significance of social media and the relationship between country and destination image, the study embarks upon to assess the cognitive, affective, conative image components. The study presents the destination image concept from the tourists’ point of view, as they review Istanbul on TripAdvisor throughout the summer in 2013. This study, although limited in scope, will be of interest to academic researchers and industry practitioners who are seeking to better understand the behavior of travelers using the Internet

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

A Business Intelligence Solution For Ticket Sales Management In Sintra’s UNESCO Cultural Heritage

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThe tourism industry has experienced huge growth of business volume in recent years leading to a rapid increase in the amount of data being generated. To leverage these data, tourism companies can greatly benefit from incorporating business intelligence tools in their daily operations, which can contribute to the creation of new value and to sustainable growth. In this work, we develop a business intelligence solution for Parques de Sintra - Monte da Lua (PSML), a public company that manages ticket sales for some of the most visited cultural attractions in Portugal, which are part of the UNESCO Cultural Heritage. We optimize their current transactional database structure for data analysis by following a dimensional modelling methodology. Then, we develop three dashboards on top of the resultant model. Each dashboard aggregates different visuals and provides information from different perspectives of the business, namely sales, attractions, and customers. We analyse the layout and visualization capabilities of the dashboards and provide insights regarding data interpretation. With this work, we provide the PSML team with a tool that can aid in the quick monitoring of their business at different levels and has the potential to inform decisions and strategies in the areas of sales, logistics, advertising, and customer satisfaction

Who booked five-star hotels in Macau? A study of hotel guests’ online booking attention

The Internet now serves as a useful tool for suppliers and consumers to communicate information and enable purchasing online. Due to its importance in the travel industry, the Internet has attracted attention from both academic researchers and industrial practitioners. Over the last two decades, various approaches have been taken to investigate travelers' online satisfaction and purchase intention. This research is the one of the first attempts that explores the demographic profile of visitors to five-star hotels in Macau, including their choice of information search channels and hotel booking options, the most frequently used online purchasing channels, and the influence of demographic characteristics on channel selection. The findings indicate a direction for future analysis of the Macau online travel market. The study shows that more than half of the respondents had made their reservations online, and the most popular channel for searching hotel information was individual hotel websites. This paper provides useful information for travel industrial managers not only about hotel guests' propensity to search and book online, but also on why some consumers did not use online channels to purchase

CULTURE-BASED INTERPRETATION OF PROJECTED DESTINATION IMAGES: A SEMANTIC NETWORK ANALYSIS

This study attempts to explore destination image interpretation in the context of two cultural groups (US and China) and two information sources (Blog and Destination Management Organization website). Semantic Network Analysis was employed to obtain a visual representation and comparison of perceived destination image categories of Marrakesh across groups. Computer-Aided Textual Analysis software, AutoMap3.0 Program and ROST Content Mining System, were used initially for data preparation and keywords analysis, and UCINET 6 was then applied to conduct semantic network analysis such as centrality analysis and network structure measurements. The results indicated that the perceived image of Marrakesh varies by the different online information sources and cultural groups. In addition, features of the two specific information sources and cultures were discussed to explain the discrepancies and similarities. The study also underscores affective attributes in destination image perception by combing both quantitative and qualitative methods in the research. Practical and theoretical implications were demonstrated to shed light on managing and marketing desired destination images

Analyzing Tourism Online Reviews: An Extended Approach to Hierarchical Topic Detection Using Keyword Clustering

Tourism managers are increasingly turning to the online sphere to gain relevant customer insights. However, current approaches to analyzing vast and rapidly changing user-generated content (UGC) face several limitations. Supervised approaches require significant effort to provide pre-tagged training data and cannot dynamically identify topics mentioned in UGC. On the other hand, unsupervised approaches typically do not support different abstraction levels or enable a successive refinement of analysis in a drill-down manner, which is often expected as a practical requirement of tourism and destination management. Our research objective is, therefore, to extend current supervised approaches for identifying predefined topics by adopting unsupervised approaches using cluster analysis. The results emphasize that unsupervised approaches can (1) detect non-predefined topics dynamically with an accuracy similar to supervised approaches, thus demonstrating the potential to replace them and avoid the necessity of providing pre-tagged training data. (2) To build a topic hierarchy, unsupervised approaches sense more fine-grained topics as an enhancement of predefined topics on a lower level of abstraction, enabling more powerful drill-down-like analyses. Overall, the proposed extended approach to topic detection promises to support tourism management by meaningfully analyzing the increasing mass of visitors’ online feedback

Information and Communication Technologies in Tourism 2022

This open access book presents the proceedings of the International Federation for IT and Travel & Tourism (IFITT)’s 29th Annual International eTourism Conference, which assembles the latest research presented at the ENTER2022 conference, which will be held on January 11–14, 2022. The book provides an extensive overview of how information and communication technologies can be used to develop tourism and hospitality. It covers the latest research on various topics within the field, including augmented and virtual reality, website development, social media use, e-learning, big data, analytics, and recommendation systems. The readers will gain insights and ideas on how information and communication technologies can be used in tourism and hospitality. Academics working in the eTourism field, as well as students and practitioners, will find up-to-date information on the status of research