@Article{Folgado+Sanz2022,
author="Folgado, M. G.
and Sanz, V.",
title="Exploring the political pulse of a country using data science tools",
journal="Journal of Computational Social Science",
year="2022",
publisher="Springernature",
volume="5",
pages="987--1000",
optkeywords="Politics; Spain; Sentiment analysis; Artificial Intelligence; Machine learning; Neural networks; Natural Language Processing (NLP)",
abstract="In this paper we illustrate the use of Data Science techniques to analyse complex human communication. In particular, we consider tweets from leaders of political parties as a dynamical proxy to political programmes and ideas. We also study the temporal evolution of their contents as a reaction to specific events. We analyse levels of positive and negative sentiment in the tweets using new tools adapted to social media. We also train a Fully-Connected Neural Network (FCNN) to recognise the political affiliation of a tweet. The FCNN is able to predict the origin of the tweet with a precision in the range of 71-75{\%}, and the political leaning (left or right) with a precision of around 90{\%}. This study is meant to be viewed as an example of how to use Twitter data and different types of Data Science tools for a political analysis.",
optnote="WOS:000742263500002",
optnote="exported from refbase (https://references.ific.uv.es/refbase/show.php?record=5077), last updated on Wed, 25 May 2022 07:36:16 +0000",
issn="2432-2717",
doi="10.1007/s42001-021-00157-1",
opturl="https://arxiv.org/abs/2011.10264",
opturl="https://doi.org/10.1007/s42001-021-00157-1",
language="English"
}