Authors:
Eric Fernandes de Mello Araújo
and
Dave Ebbelaar
Affiliation:
VU Amsterdam, Netherlands
Keyword(s):
Text Mining, Twitter, Politics, Dutch politics, Machine Learning, Natural Language Processing.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Computational Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Machine Learning
;
Natural Language Processing
;
Pattern Recognition
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
The task of classifying political tweets has been shown to be very difficult, with controversial results in many
works and with non-replicable methods. Most of the works with this goal use rule-based methods to identify
political tweets. We propose here two methods, being one rule-based approach, which has an accuracy of 62%,
and a supervised learning approach, which went up to 97% of accuracy in the task of distinguishing political
and non-political tweets in a corpus of 2.881 Dutch tweets. Here we show that for a data base of Dutch tweets,
we can outperform the rule-based method by combining many different supervised learning methods.