document: Cross-domain sentiment classification via spectral feature alignment Sentiment classification aims to automatically predict sentiment polarity ( e.g. , positive or negative ) of users publishing sentiment data ( e.g. , reviews , blogs ) . Although traditional classification algorithms can be used to train sentiment classifiers from manually labeled text data , the labeling work can be time-consuming and expensive . Meanwhile , users often use some different words when they express sentiment in different domains . If we directly apply a classifier trained in one domain to other domains , the performance will be very low due to the differences between these domains . In this work , we develop a general solution to sentiment classification when we do not have any labels in a target domain but have some labeled data in a different domain , regarded as source domain . In this cross-domain sentiment classification setting , to bridge the gap between the domains , we propose a spectral feature alignment ( SFA ) algorithm to align domain-specific words from different domains into unified clusters , with the help of domain-independent words as a bridge . In this way , the clusters can be used to reduce the gap between domain-specific words of the two domains , which can be used to train sentiment classifiers in the target domain accurately . Compared to previous approaches , SFA can discover a robust representation for cross-domain data by fully exploiting the relationship between the domain-specific and domain-independent words via simultaneously co-clustering them in a common latent space . We perform extensive experiments on two real world datasets , and demonstrate that SFA significantly outperforms previous approaches to cross-domain sentiment classification .
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