Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts

Alexandra Balahur, Marco Turchi, Ralf Steinberger, Jose-Manuel Perea-Ortega, Guillaume Jacquet, Dilek Küçük, Vanni Zavarella, Adil El Ghali

Abstract
This paper presents an evaluation of the use of machine translation to obtain and employ data for training multilingual sentiment classifiers. We show that the use of machine translated data obtained similar results as the use of native-speaker translations of the same data. Additionally, our evaluations pinpoint to the fact that the use of multilingual data, including that obtained through machine translation, leads to improved results in sentiment classification. Finally, we show that the performance of the sentiment classifiers built on machine translated data can be improved using original data from the target language and that even a small amount of such texts can lead to significant growth in the classification performance.
Anthology ID:
L14-1727
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
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Pages:
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/965_Paper.pdf
DOI:
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Cite (ACL):
Alexandra Balahur, Marco Turchi, Ralf Steinberger, Jose-Manuel Perea-Ortega, Guillaume Jacquet, Dilek Küçük, Vanni Zavarella, and Adil El Ghali. 2014. Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts (Balahur et al., LREC 2014)
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http://www.lrec-conf.org/proceedings/lrec2014/pdf/965_Paper.pdf
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