TY  - JOUR
AU  - Folgado, M. G.
AU  - Sanz, V.
PY  - 2022
DA  - 2022//
TI  - Exploring the political pulse of a country using data science tools
T2  - J. Comput. Soc. Sci.
JO  - Journal of Computational Social Science
SP  - 987
EP  - 1000
VL  - 5
PB  - Springernature
KW  - Politics
KW  - Spain
KW  - Sentiment analysis
KW  - Artificial Intelligence
KW  - Machine learning
KW  - Neural networks
KW  - Natural Language Processing (NLP)
AB  - 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.
SN  - 2432-2717
UR  - https://arxiv.org/abs/2011.10264
UR  - https://doi.org/10.1007/s42001-021-00157-1
DO  - 10.1007/s42001-021-00157-1
LA  - English
N1  - WOS:000742263500002
ID  - Folgado+Sanz2022
ER  -