Political News Sentiment Analysis for Under-resourced Languages

Patrik F. Bakken, Terje A. Bratlie, Cristina Marco, Jon Atle Gulla


Abstract
This paper presents classification results for the analysis of sentiment in political news articles. The domain of political news is particularly challenging, as journalists are presumably objective, whilst at the same time opinions can be subtly expressed. To deal with this challenge, in this work we conduct a two-step classification model, distinguishing first subjective and second positive and negative sentiment texts. More specifically, we propose a shallow machine learning approach where only minimal features are needed to train the classifier, including sentiment-bearing Co-Occurring Terms (COTs) and negation words. This approach yields close to state-of-the-art results. Contrary to results in other domains, the use of negations as features does not have a positive impact in the evaluation results. This method is particularly suited for languages that suffer from a lack of resources, such as sentiment lexicons or parsers, and for those systems that need to function in real-time.
Anthology ID:
C16-1281
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2989–2996
Language:
URL:
https://aclanthology.org/C16-1281
DOI:
Bibkey:
Cite (ACL):
Patrik F. Bakken, Terje A. Bratlie, Cristina Marco, and Jon Atle Gulla. 2016. Political News Sentiment Analysis for Under-resourced Languages. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2989–2996, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Political News Sentiment Analysis for Under-resourced Languages (Bakken et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-1281.pdf