Exploring the political pulse of a country using data science tools

TL;DR

This paper demonstrates how data science techniques can analyze political communication on social media, revealing insights into political sentiments, affiliations, and reactions to events through tweet analysis.

Contribution

It introduces new social media analysis tools and AI models to classify political affiliation and sentiment with high accuracy, bridging data science and political analysis.

Findings

AI predicts political origin with 71-75% accuracy

Political leaning classification achieves around 90% precision

Sentiment analysis tracks temporal evolution of political discourse

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 an Artificial Intelligence to recognise the political affiliation of a tweet. The AI 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 a proof-of-concept of interdisciplinary nature, at the interface between Data Science and political analysis.