Sentiment after translation: a case-study on Arabic social media posts

DOIResolve DOI: http://doi.org/10.3115/v1/N15-1078
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TypeArticle
Proceedings titleProceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Conference2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, May 31–June 5, 2015, Denver, Colorado, USA
ISBN978-1-941643-49-5
Article numberN15-1078
Pages767777
AbstractWhen text is translated from one language into another, sentiment is preserved to varying degrees. In this paper, we use Arabic social media posts as stand-in for source language text, and determine loss in sentiment predictability when they are translated into English, manually and automatically. As benchmarks, we use manually and automatically determined sentiment labels of the Arabic texts. We show that sentiment analysis of English translations of Arabic texts produces competitive results, w.r.t. Arabic sentiment analysis. We discover that even though translation significantly reduces the human ability to recover sentiment, automatic sentiment systems are still able to capture sentiment information from the translations
Publication date
PublisherAssociation for Computational Linguistics
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number23000028
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Record identifierb9431a3a-e900-4176-9f67-08ad6cd85cae
Record created2016-05-30
Record modified2016-05-30
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