Abstract
Despite the Cultural Tourism experience phenomenon being far from understood, this chapter proposes an innovative approach that uses Google Maps data for real-time monitoring, which shows the capacity to estimate and visualize the geographical distribution of Google Places most relevant to the Cultural Tourism domain. By doing so, this approach uncovers insights about the geographical distribution of Cultural Tourism’s offerings, thereby shedding light on the diversity and peculiarities of dispersion patterns of Cultural Tourism attractions throughout Sweden. The objectives of this chapter are: (a) to examine the state of the art of tourism research using big data gained from Google Maps; (b) to extract and use Google Maps-based supply-side data in the Cultural Tourism domain for interactive (near-) real-time tourism monitoring; (c) to extract and analyze visitors’ online feedback data gained from Google Maps in the Cultural Tourism domain; and (d) to unravel future veins of research in the domain of Google-Maps-based tourism monitoring and analytics. This chapter illustrates how user-generated content extracted from Google Maps can be used to automatically evaluate the complex experience outcomes of travelers’ Cultural Tourism encounters by applying a lexicon-based sentiment analysis technique as well as a machine-learning-based topic modeling approach, respectively. The proposed approach shows that the most popular places along with the most frequent opinion words can be exhibited on geographical maps. The potential of Google Maps data for reliably monitoring the fragmented Cultural Tourism supply is demonstrated, as well as for assessing travelers’ complex, worldview-dependent, and socially constructed Cultural Tourism experience outcomes.