The sentiment classification performance relies on high-quality sentiment resources . However , these resources are imbalanced in different languages . Cross-language sentiment classification ( CLSC ) can leverage the rich resources in one language ( source language ) for sentiment classification in a resource-scarce language ( target language ) . Bilingual embeddings could eliminate the semantic gap between two languages for CLSC , but ignore the sentiment information of text . This paper proposes an approach to learning bilingual sentiment word embeddings ( BSWE ) for English-Chinese CLSC . The proposed B-SWE incorporate sentiment information of text into bilingual embeddings . Furthermore , we can learn high-quality BSWE by simply employing labeled corpora and their translations , without relying on largescale parallel corpora . Experiments on NLP&CC 2013 CLSC dataset show that our approach outperforms the state-of-theart systems . Sentiment classification is a task of predicting sentiment polarity of text , which has attracted considerable interest in the NLP field . To date , a number of corpus-based approaches ( Pang et al . , 2002 ; Pang and Lee , 2004 ; Kennedy and Inkpen , 2006 ) have been developed for sentiment classification . The approaches heavily rely on quality and quantity of the labeled corpora , which are considered as the most valuable resources in sentiment classification task . However , such sentiment resources are imbalanced in different languages . To leverage resources in the source language to improve the sentiment classification performance in the target language , cross-language sentiment classification ( CLSC ) approaches have been investigated . The traditional CLSC approaches employ machine translation ( MT ) systems to translate corpora in the source language into the target language , and train the sentiment classifiers in the target language ( Banea et al . , 2008 ) . Directly employing the translated resources for sentiment classification in the target language is simple and could get acceptable results . However , the gap between the source language and target language inevitably impacts the performance of sentiment classification . To improve the classification accuracy , multiview approaches have been proposed . In these approaches , the resources in the source language and their translations in the target language are both used to train sentiment classifiers in two independent views ( Wan , 2009 ; Gui et al . , 2013 ; Zhou et al . , 2014a ) . The final results are determined by ensemble classifiers in these two views to overcome the weakness of monolingual classifiers . However , learning language-specific classifiers in each view fails to capture the common sentiment information of two languages during training process . With the revival of interest in deep learning ( Hinton and Salakhutdinov , 2006 ) , shared deep representations ( or embeddings ) ( Bengio et al . , 2013 ) are employed for CLSC ( Chandar A P et al . , 2013 ) . Usually , paired sentences from parallel corpora are used to learn word embeddings across languages ( Chandar A P et al . , 2013 ; Chandar A P et al . , 2014 ) , eliminating the need of MT systems . The learned bilingual embeddings could easily project the training data and test data into a common space , where training and testing are performed . However , high-quality bilingual embeddings rely on the large-scale task-related parallel corpora , which are not always readily available . Meanwhile , though semantic similarities across languages are captured during bilingual embedding learning process , sentiment information of text is ignored . That is , bilingual embeddings learned from unlabeled parallel corpora are not effective enough for CLSC because of a lack of explicit sentiment information . Tang and Wan ( 2014 ) first proposed a bilingual sentiment embedding model using the original training data and the corresponding translations through a linear mapping rather than deep learning technique . This paper proposes a denoising autoencoder based approach to learning bilingual sentiment word embeddings ( BSWE ) for CLSC , which incorporates sentiment polarities of text into the bilingual embeddings . The proposed approach learns BSWE with the original labeled documents and their translations instead of parallel corpora . The BSWE learning process consists of two phases : the unsupervised phase of semantic learning and the supervised phase of sentiment learning . In the unsupervised phase , sentiment words and their negation features are extracted from the source training data and their translations to represent paired documents . These features are used as inputs for a denoising autoencoder to learn the bilingual embeddings . In the supervised phase , sentiment polarity labels of documents are used to guide BSWE learning for incorporating sentiment information into the bilingual embeddings . The learned BSWE are applied to project English training data and Chinese test data into a common space . In this space , a linear support vector machine ( SVM ) is used to perform training and testing . The experiments are carried on NLP&CC 2013 CLSC dataset , including book , DVD and music categories . Experimental results show that our approach achieves 80.68 % average accuracy , which outperforms the state-of-the-art systems on this dataset . Although the BSWE are only evaluated on English-Chinese CLSC here , it can be popularized to many other languages . The major contributions of this work can be summarized as follows : • We propose bilingual sentiment word embeddings ( BSWE ) for CLSC based on deep learning technique . Experimental results show that the proposed BSWE significantly outperform the bilingual embeddings by incorporating sentiment information . • Instead of large-scale parallel corpora , only the labeled English corpora and Englishto-Chinese translations are required for B-SWE learning . It is proved that in spite of the small-scale of training set , our approach outperforms the state-of-the-art systems in NLP&CC 2013 CLSC share task . • We employ sentiment words and their negation features rather than all words in documents to learn sentiment-specific embeddings , which significantly reduces the dimension of input vectors as well as improves sentiment classification performance . This paper proposes an approach to learning B-SWE by incorporating sentiment information into the bilingual embeddings for CLSC . The proposed approach learns BSWE with the labeled documents and their translations rather than parallel corpora . In addition , BDR is proposed to enhance the sentiment expression ability which combines English and Chinese representations . Experiments on the NLP&CC 2013 CLSC dataset show that our approach outperforms the previous stateof-the-art systems as well as traditional bilingual embedding systems . The proposed BSWE are only evaluated on English-Chinese CLSC in this paper , but it can be popularized to other languages . Both semantic and sentiment information play an important role in sentiment classification . In the following work , we will further investigate the relationship between semantic and sentiment information for CLSC , and balance their functions to optimize their combination for CLSC .

Challenge: Bilingual word embeddings trained on unlabeled corpus lack explicit information for sentiment classification however task-related parallel corpus is not always available.
Approach: They propose to first extract sentiment words from training data and their translations then use the results to guide word embeddings to incorporate sentiment information.
Outcome: The proposed method achieves 80.68% average accuracy on the NLP&CC 2013 CLSC dataset which outperforms the state-of-the-art and traditional bilingual embedding systems.