Happiness is a Choice: Sentiment and Activity-Aware Location Recommendation

Jia Wang, Yungang Feng, Elham Naghizade, Lida Rashidi, Kwan Hui Lim, Kate E. Lee

Abstract: Studying large, widely spread Twitter data has laid the foundation for many novel applications from predicting natural disasters and epidemics to understanding urban dynamics. Recent studies have focused on exploring people's emotional response to their urban environment, e.g., green spaces versus built up areas, through analysing the sentiment of tweets within that area. Since green spaces have the capacity to improve citizen's well-being, we developed a system that is capable of recommending green spaces to users. Our system is unique in the sense that the recommendations are tailored with regard to users' preferred activity as well as the degree of positive sentiments in each green space. We show that the incoming flow of tweets can be used to refine the recommendations over time. Furthermore, We implemented a web-based, user-friendly interface to solicit user inputs and display recommendation results.