Location-based Sentiment Analyses and Visualization of Twitter Election Data

Abstract

In this article, we perform sentiment analyses of Twitter location data. We use two case studies: US presidential elections of 2016 and UK general elections of 2017. For US elections, we plot state-wise user sentiment towards Hillary Clinton and Donald Trump. For UK elections, we download two disparate datasets, using keywords and location coordinates, looking for similar tendencies in sentiment towards political candidates and parties. The overall objective of the two case studies is to evaluate similarity between sentiment of location-based tweets and on-ground public opinion reflected in election results. We discover Twitter location sentiment does indeed corroborate with the election result in both cases. We also discover similar tendencies in Twitter sentiment towards political candidates and parties regardless of the methodology adopted for data collection.