Text Mining Tourism Literature

Literature reviews are essential for understanding a specific domain as they map the main topics of current re-search. Our aim was to provide a framework for retrieving articles from online databases and analyzing them in a single script. We provide the analytical pipeline as open-source (https://github.com/tourism4-0/BibMine). The main research focus was on analyzing 318 abstracts from scientific papers on tourism and innovation, which we report in Zach et al. (2019). We used LDA topic modeling to uncover ten main topics, which we analyzed using pyLDAvis visualization. We used saliency and relevance scores to determine the main words that de-scribe a topic. The uncovered topics range from climate change and land use to smart destinations, travel expe-riences, and ICT. We performed similar analyses for the term "stakeholders," where we also observed the main verbs related to the query. Since verbs best define an activity, we used them to determine how stakeholders are involved in tourism development. Finally, we analyzed papers with the keyword "technology," where energy efficiency, VR, web technology, and augmented tourist experiences were the main topics.