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

Published: 16 June 2020 Publication History

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.

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cover image ACM Other conferences
dg.o '20: Proceedings of the 21st Annual International Conference on Digital Government Research
June 2020
389 pages
ISBN:9781450387910
DOI:10.1145/3396956
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 16 June 2020

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Author Tags

  1. Currency fluctuation
  2. Machine Learning
  3. Sentiment Analysis
  4. Topic Modeling
  5. Twitter
  6. Unsupervised Learning

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