Cross-city Analysis of Location-based Sentiment in User-generated Text

Geolocated user-generated content is a promising source of data reflecting how citizens live and feel. Information extracted from this source is being increasingly used for urban planning and policy evaluation purposes. While a lot of existing research focuses on the relationship between locations and sentiment in social media postings, we aim to uncover relations between location and sentiment that are consistent over cities around the world. In this paper, we therefore analyze the relationship between multiple categories of points of interest (POIs) in the OpenStreetMap dataset and the sentiment of English microblogging messages sent nearby using a three-stage processing pipeline: (1) extract sentiment scores from geolocated microblogs posted on Twitter, (2) spatial aggregation of sentiment in cities and POIs, (3) analyze relationships in aggregated sentiment. We identify differences in Twitter users' sentiments within cities based on POIs, and we investigate the temporal dynamics of these sentiments and compare our findings between major cities in multiple countries.