Visualizing theme park visitors’ emotions using social media analytics and geospatial analytics

Abstract

Despite the popular use of social media analytics to scrutinize customer emotions, less scholarly efforts have been invested in visualizing theme park visitors’ emotions. Employing the convergence of social media analytics and geospatial analytics, this paper visualized cohesive places where Disneyland visitors express distinct types of emotion in social media messages. Among 226,946 collected tweets, this study used 19,809 tweets containing one or more emotion words listed in Russell’s Circumplex Model of Affect. Text mining analysis and GIS-based exploratory spatial data analysis showed that tweets reflecting each quadrant of emotions have considerable spatial variations and different topics related to visitor emotions. The research approach enabled displaying particular spots in theme park zones and areas of riding attractions where emotions of each quadrant are significantly clustered. This study highlights methodological implications of visualizing spatial patterns of visitors’ emotions and provides practitioners with a useful guide to develop routes evoking pleasant emotions.