An Analysis of Brexit With the MonkeyLearn Machine Learning API

The result of the UK’s recent referendum to leave the EU has raised question marks over the fate of the European Union. Many people are wondering whether the Brexit decision will trigger another recession, pave the way for Scottish independence, or begin the demise of the EU as a whole. With so much uncertainty surrounding the possible outcomes, Federico Pascual from MonkeyLearn published a machine learning analysis of the Brexit result.

The analysis is based on what people are saying about Brexit in more than 450,000 tweets using the hashtag #Brexit on Twitter. They filtered out the non-English tweets, leaving around 250,000, then ran a MonkeyLearn analysis using ready-to-use machine learning models and sentiment analysis to identify whether the tone was positive, negative or neutral. They also performed some keyword extraction to get more context of what people were saying.

The results showed mixed sentiments of 63,024 positive tweets, with 70,581 negative tweets. The positive results were thankful and saw the result as a ‘good thing’. Negative tweets expressed sadness and anger at the result, with many expressing concern over Britain’s future.

The post also presents the positive, negative and neutral figures of tweets for the topics Scotland, Democracy, Donald Trump, David Cameron, and Nigel Farage, the UKIP (UK Independence Party). The article also shows the most relevant keywords for both positive and negative results, and the analysis’ source code is available on GitHub.

Pascual ends off by highlighting the relevancy of Donald Trump mentions, suggesting this may represent a global shift toward conservatism. Whatever the future holds, the polarity of public opinion on the Brexit results shows that the United Kingdom is anything but.

Be sure to read the next Machine Learning article: What are the Best Python Tools for Machine Learning?

Original Article

The Divided Kingdom: a machine learning analysis on the Brexit result

 

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