Analyzing Brexit’s impact using sentiment analysis and topic modeling on Twitter discussion

Abstract

In this paper we evaluate public sentiment and opinion on Brexit during September and October 2019 by collecting over 16 million user messages from Twitter - world’s largest online micro-blogging service. We perform sentiment analysis using the Python VADER library, and topic modeling using Latent Dirichlet Allocation function of the gensim library. Through sentiment analysis, we quantify daily public sentiment towards Brexit and use it to evaluate Brexit’s impact on the British currency exchange rate and stock markets in Britain. With the aid of topic modeling, we discover the most popular daily topics of discussion on Twitter using the keyword ”Brexit”. Some of our findings include the discovery of positive correlation between Twitter sentiment towards Brexit and British pound sterling exchange rate. We also found daily discussion topics on Twitter, identified through unsupervised machine learning to be a good proxy of important current events related with Brexit.