Natural language processing meets spatial time series analysis and geovisualization: identifying and visualizing spatio-topical sentiment trends on Twitter

Abstract

ABSTRACT Previous studies have introduced various approaches for visualizing the spatial and temporal distributions of sentiments expressed on social media. However, many existing methods either overlook the evolving nature of sentiments or fail to account for the spatial distribution of sentiment trends related to specific topics. To gain a comprehensive understanding of how sentiments evolve in relation to topics and geographies, it is essential to capture the dynamic nature of sentiment through time series analysis and geovisualization. This article introduces a workflow that combines natural language processing, spatial time series analysis, and geovisualization techniques to identify and visualize the variations in sentiment trends on Twitter across different geographic regions and topics. By examining the 2016 presidential debates as a case study, we uncover distinct temporal patterns in sentiment distributions across various topics and states. Our findings also show that adjacent states do not always share similar sentiment trends, and that geographic clusters with similar sentiment trends also vary across topics. Failing to consider these variations may result in misunderstanding public discourse and sentiments since they are diverse and dynamic in nature.