Sentiment classification aims to automatically predict sentiment polarity ( e.g. , positive or negative ) of user generated sentiment data ( e.g. , reviews , blogs ) . Due to the mismatch among different domains , a sentiment classifier trained in one domain may not work well when directly applied to other domains . Thus , domain adaptation for sentiment classification algorithms are highly desirable to reduce the domain discrepancy and manual labeling costs . To address the above challenge , we propose a novel domain adaptation method , called Bi-Transferring Deep Neural Networks ( BTDNNs ) . The proposed BTDNNs attempts to transfer the source domain examples to the target domain , and also transfer the target domain examples to the source domain . The linear transformation of BTDNNs ensures the feasibility of transferring between domains , and the distribution consistency between the transferred domain and the desirable domain is constrained with a linear data reconstruction manner . As a result , the transferred source domain is supervised and follows similar distribution as the target domain . Therefore , any supervised method can be used on the transferred source domain to train a classifier for sentiment classification in a target domain . We conduct experiments on a benchmark composed of reviews of 4 types of Amazon products . Experimental results show that our proposed approach significantly outperforms the several baseline methods , and achieves an accuracy which is competitive with the state-of-the-art method for domain adaptation . With the rise of social media ( e.g. , blogs and social networks etc . ) , more and more user generated sentiment data have been shared on the Web ( Pang et al . , 2002 ; Pang and Lee , 2008 ; Liu , 2012 ; Zhou et al . , 2011 ) . They exist in the form of user reviews on shopping or opinion sites , in posts of blogs / questions or customer feedbacks . This has created a surge of research in sentiment classification ( or sentiment analysis ) , which aims to automatically determine the sentiment polarity ( e.g. , positive or negative ) of user generated sentiment data ( e.g. , reviews , blogs , questions ) . Machine learning algorithms have been proved promising and widely used for sentiment classification ( Pang et al . , 2002 ; Pang and Lee , 2008 ; Liu , 2012 ) . However , the performance of these models relies on manually labeled training data . In many practical cases , we may have plentiful labeled data in the source domain , but very few or no labeled data in the target domain with a different data distribution . For example , we may have many labeled books reviews , but we are interested in detecting the polarity of electronics reviews . Reviews for different products might have different vocabularies , thus classifiers trained on one domain often fail to produce satisfactory results when transferring to another domain . This has motivated much research on cross-domain ( domain adaptation ) sentiment classification which transfers the knowledge from the source domain to the target domain ( Thomas et al . , 2006 ; Snyder and Barzilay , 2007 ; Blitzer et al . , 2006 ; Blitzer et al . , 2007 ; Daume III , 2007 ; Li and Zong , 2008 ; Li et al . , 2009 ; Pan et al . , 2010 ; Kumar et al . , 2010 ; Glorot et al . , 2011 ; Chen et al . , 2011a ; Chen et al . , 2012 ; Li et al . , 2012 ; Xia et al . , 2013a ; Li et al . , 2013 ; Zhou et al . , 2015a ; Zhuang et al . , 2015 ) . Depending on whether the labeled data are available for the target domain , cross-domain sen-timent classification can be divided into two categories : supervised domain adaptation and unsupervised domain adaptation . In scenario of supervised domain adaptation , labeled data is available in the target domain but the number is usually too small to train a good sentiment classifier , while in unsupervised domain adaptation only unlabeled data is available in the target domain , which is more challenging . This work focuses on the unsupervised domain adaptation problem of which the essence is how to employ the unlabeled data of target domain to guide the model learning from the labeled source domain . The fundamental challenge of cross-domain sentiment classification lies in that the source domain and the target domain have different data distribution . Recent work has investigated several techniques for alleviating the domain discrepancy : instance-weight adaptation ( Huang et al . , 2007 ; Jiang and Zhai , 2007 ; Li and Zong , 2008 ; Mansour et al . , 2009 ; Dredze et al . , 2010 ; Chen et al . , 2011b ; Chen et al . , 2011a ; Chen et al . , 2012 ; Li et al . , 2013 ; Xia et al . , 2013a ) and feature representation adaptation ( Thomas et al . , 2006 ; Snyder and Barzilay , 2007 ; Blitzer et al . , 2006 ; Blitzer et al . , 2007 ; Li et al . , 2009 ; Pan et al . , 2010 ; Zhou et al . , 2015a ; Zhuang et al . , 2015 ) . The first kind of methods assume that some training data in the source domain are very useful for the target domain and these data can be used to train models for the target domain after re-weighting . In contrast , feature representation approaches attempt to develop an adaptive feature representation that is effective in reducing the difference between domains . Recently , some efforts have been initiated on learning robust feature representations with deep neural networks ( DNNs ) in the context of crossdomain sentiment classification ( Glorot et al . , 2011 ; Chen et al . , 2012 ) . Glorot et al . ( 2011 ) proposed to learn robust feature representations with stacked denoising auto-encoders ( SDAs ) ( Vincent et al . , 2008 ) . Denoising auto-encoders are onelayer neural networks that are optimized to reconstruct input data from partial and random corruption . These denoisers can be stacked into deep learning architectures . The outputs of their intermediate layers are then used as input features for SVMs ( Fan et al . , 2008 ) . Chen et al . ( 2012 ) proposed a marginalized SDA ( mSDA ) that addressed the two crucial limitations of SDAs : high computational cost and lack of scalability to highdimensional features . However , these methods learn the unified domain-invariable feature representations by combining the source domain data and that of the target domain data together , which can not well characterize the domain-specific features as well as the commonality of domains . To this end , we propose a Bi-Transferring Deep Neural Networks ( BTDNNs ) which can transfer the source domain examples to the target domain and also transfer the target domain examples to the source domain , as shown in Figure 1 . In BTDNNs , the linear transformation makes the feasibility of transferring between domains , and the linear data reconstruction manner ensures the distribution consistency between the transferred domain and the desirable domain . Specifically , our BTDNNs has one common encoder f c , two decoders g s and g t which can map an example to the source domain and the target domain respectively . As a result , the source domain can be transferred to the target domain along with its sentiment label , and any supervised method can be used on the transferred source domain to train a classifier for sentiment classification in the target domain , as the transferred source domain data share the similar distribution as the target domain . Experimental results show that the proposed approach significantly outperforms several baselines , and achieves an accuracy which is competitive with the state-of-the-art method for cross-domain sentiment classification . The remainder of this paper is organized as follows . Section 2 introduces the related work . Section 3 describes our proposed bi-transferring deep neural networks ( BTDNNs ) . Section 4 presents the experimental results . In Section 5 , we conclude with ideas for future research . In this paper , we propose a novel Bi-Transferring Deep Neural Networks ( BTDNNs ) for crossdomain sentiment classification . The proposed BTDNNs attempts to transfer the source domain examples to the target domain , and also transfer the target domain examples to the source domain . The linear transformation of BTDNNs ensures the feasibility of transferring between domains , and the distribution consistency between the transferred domain and the desirable domain is constrained with a linear data reconstruction manner . Experimental results show that BTDNNs significantly outperforms the several baselines , and achieves an accuracy which is competitive with the state-of-the-art method for sentiment classification adaptation . There are some ways in which this research could be continued . First , since deep learning may obtain better generalization on large-scale data sets ( Bengio , 2009 ) , a straightforward path of the future research is to apply the proposed BTDNNs for domain adaptation on a much larger industrial-strength data set of 22 domains ( Glorot et al . , 2011 ) . Second , we will try to investigate the use of the proposed approach for other kinds of data set , such as 20 newsgroups and Reuters-21578 ( Li et al . , 2012 ; Zhuang et al . , 2013 ) .

Challenge: Sentiment classifiers suffer from out-of-domain inputs and labelled data for supervised domain adaption is not often available limiting the applicability of such a model.
Approach: They propose a cross-domain adaptation method which can map examples between source and target domains with their labels by a linear transformation.
Outcome: The proposed model outperforms several baseline methods and is competitive with the state-of-the-art method on sentiment classification tasks from 4 types of Amazon products.