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.
Original language | English |
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Title of host publication | ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence |
Editors | Ana Paula Rocha, Jaap van den Herik |
Place of Publication | Setúbal |
Publisher | SciTePress |
Pages | 462-469 |
Number of pages | 8 |
Volume | 2 |
ISBN (Electronic) | 9789897582752 |
DOIs | |
Publication status | Published - Jan 2018 |
Event | 10th International Conference on Agents and Artificial Intelligence, ICAART 2018 - Funchal, Madeira, Portugal Duration: 16 Jan 2018 → 18 Jan 2018 |
Conference
Conference | 10th International Conference on Agents and Artificial Intelligence, ICAART 2018 |
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Country/Territory | Portugal |
City | Funchal, Madeira |
Period | 16/01/18 → 18/01/18 |
Keywords
- Dutch politics
- Machine Learning
- Natural Language Processing
- Politics
- Text Mining