@InProceedings{xu-wan:2017:EMNLP2017,
  author    = {Xu, Kui  and  Wan, Xiaojun},
  title     = {Towards a Universal Sentiment Classifier in Multiple languages},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {511--520},
  abstract  = {Existing sentiment classifiers usually work for only one specific language, and
	different classification models are used in different languages. In this paper
	we aim to build a universal sentiment classifier with a single classification
	model in multiple different languages. In order to achieve this goal, we
	propose to learn multilingual sentiment-aware word embeddings simultaneously
	based only on the labeled reviews in English and unlabeled parallel data
	available in a few language pairs. It is not required that the parallel data
	exist between English and any other language, because the sentiment information
	can be transferred into any language via pivot languages. We present the
	evaluation results of our universal sentiment classifier in five languages, and
	the results are very promising even when the parallel data between English and
	the target languages are not used. Furthermore, the universal single classifier
	is compared with a few cross-language sentiment classifiers relying on direct
	parallel data between the source and target languages, and the results show
	that the performance of our universal sentiment classifier is very promising
	compared to that of different cross-language classifiers in multiple target
	languages.},
  url       = {https://www.aclweb.org/anthology/D17-1053}
}