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1. Introduction
Visitor satisfaction is an essential ingredient for destination performance and competitiveness (Abreu Novais et al., 2018). Destinations are interested in benchmarking their performance against their own past performance, independent standards or competitor destinations. As Kozak (2002, p. 500) argues, the:
[…] concepts of performance and satisfaction are strongly interrelated, because achievements in the former lead to the latter. Therefore, feedback received from customers is regarded as a suitable way of comparing the performance of an organization to that of another.
Positive experiences have also been linked to more positive word of mouth and intention to revisit a destination (Abubakar et al., 2017; Cong, 2016).
Obtaining data on visitor satisfaction, however, is costly. Traditional approaches require data collection through tourist surveys (Kozak, 2002). Many studies use quantitative questionnaires as the key instrument, although some studies have used case studies, interviews and participant observation. Surveying visitors has several drawbacks. Because the visitors made a high investment in their travel, their responses may reflect an inherently positive assessment (i.e. confirmation bias) (Dodds et al., 2015). Interviewer bias and culturally moderated ways of answering questions are also known problems. In addition, questionnaires often lack comprehensiveness. They also tend to be based on small samples and have to be repeated regularly.
As an alternative, the fast increasing volume of online user-generated content (UGC) offers a promising alternative. New technologies provide greater and more convenient access to a wide variety of data (Sigala and Gretzel, 2018). Travellers are now able to share their experiences with others on various types of internet-based platforms, creating a substantial amount of electronic word of mouth (e-WOM). Whilst personal advice often ranks as the most influential source, the overall reliability and credibility of e-WOM compared to traditional WOM is adequate (Xiang et al., 2015).
Given the exponential growth of e-WOM, effectively searching, manipulating and aggregating such data is challenging (Xiang et al., 2015). One promising techniques to tackle these challenges is sentiment analysis; increasingly used for so-called social listening (Munezero et al., 2014). Sentiment analysis has attracted particular interest amongst tourism managers and marketers, and many commercial products are now available to provide some form of social media monitoring. Typically, these tools consider “number of mentions” and sentiment...