%0 Generic %1 asgari2019unisent %A Asgari, Ehsaneddin %A Braune, Fabienne %A Ringlstetter, Christoph %A Mofrad, Mohammad R. K. %D 2019 %K kallimachos sentimentanalysis %T UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages %U http://arxiv.org/abs/1904.09678 %X In this paper, we introduce UniSent a universal sentiment lexica for 1000 languages created using an English sentiment lexicon and a massively parallel corpus in the Bible domain. To the best of our knowledge, UniSent is the largest sentiment resource to date in terms of number of covered languages, including many low resource languages. To create UniSent, we propose Adapted Sentiment Pivot, a novel method that combines annotation projection, vocabulary expansion, and unsupervised domain adaptation. We evaluate the quality of UniSent for Macedonian, Czech, German, Spanish, and French and show that its quality is comparable to manually or semi-manually created sentiment resources. With the publication of this paper, we release UniSent lexica as well as Adapted Sentiment Pivot related codes. method.

@misc{asgari2019unisent, abstract = {In this paper, we introduce UniSent a universal sentiment lexica for 1000 languages created using an English sentiment lexicon and a massively parallel corpus in the Bible domain. To the best of our knowledge, UniSent is the largest sentiment resource to date in terms of number of covered languages, including many low resource languages. To create UniSent, we propose Adapted Sentiment Pivot, a novel method that combines annotation projection, vocabulary expansion, and unsupervised domain adaptation. We evaluate the quality of UniSent for Macedonian, Czech, German, Spanish, and French and show that its quality is comparable to manually or semi-manually created sentiment resources. With the publication of this paper, we release UniSent lexica as well as Adapted Sentiment Pivot related codes. method.}, added-at = {2019-04-30T15:06:21.000+0200}, author = {Asgari, Ehsaneddin and Braune, Fabienne and Ringlstetter, Christoph and Mofrad, Mohammad R. K.}, biburl = {https://www.bibsonomy.org/bibtex/27198513353f436172de6e2af0f120199/albinzehe}, description = {UniSent: Universal Adaptable Sentiment Lexica for 1000 Languages}, interhash = {7659fa6c4de9636041763dbb94b9cfb1}, intrahash = {7198513353f436172de6e2af0f120199}, keywords = {kallimachos sentimentanalysis}, note = {cite arxiv:1904.09678}, timestamp = {2019-04-30T15:06:21.000+0200}, title = {UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages}, url = {http://arxiv.org/abs/1904.09678}, year = 2019 }