Abstract
Review platforms are gaining more and more importance in the tourism industry. From a consumer’s point of view, reviews facilitate information search and influence the decision making process. Service providers are unable to neglect the impact of such websites and are thus forced to track reviews. However, due to the massive load of reviews, this task becomes more and more time consuming. Text mining tools assist in extracting decision-relevant knowledge from user-generated content (UGC). In order to assess the appropriateness of machine-driven approaches, TripAdvisor reviews of restaurants as well as hotels were collected and analysed applying the AYLIEN Text Analysis API on RapidMiner. The conclusions hereof were then compared with results generated by traditional manual content analysis. Findings support the adequacy of fully automated domain specific aspect-based sentiment analysis tools. The authors argue that the suggested methodology facilitates the analysis dramatically and can thus be simply applied on a regular basis with the aim to constantly monitor reviews.
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Weismayer, C., Pezenka, I., Gan, C.HK. (2018). Aspect-Based Sentiment Detection: Comparing Human Versus Automated Classifications of TripAdvisor Reviews. In: Stangl, B., Pesonen, J. (eds) Information and Communication Technologies in Tourism 2018. Springer, Cham. https://doi.org/10.1007/978-3-319-72923-7_28
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