@inproceedings{bakken-etal-2016-political,
title = "Political News Sentiment Analysis for Under-resourced Languages",
author = "Bakken, Patrik F. and
Bratlie, Terje A. and
Marco, Cristina and
Gulla, Jon Atle",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1281",
pages = "2989--2996",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Political News Sentiment Analysis for Under-resourced Languages
%A Bakken, Patrik F.
%A Bratlie, Terje A.
%A Marco, Cristina
%A Gulla, Jon Atle
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F bakken-etal-2016-political
%X 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.
%U https://aclanthology.org/C16-1281
%P 2989-2996
Markdown (Informal)
[Political News Sentiment Analysis for Under-resourced Languages](https://aclanthology.org/C16-1281) (Bakken et al., COLING 2016)
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.