The fact that the J ST model does not required any labeled documents for training makes it desirable for domain adaptation in sentiment classification.

We proceed with a review of related work on sentiment domain adaptation .

We subsequently show that words from different domains can indeed be grouped under the same polarity-bearing topic through an illustration of example topic words extracted by J ST before proposing a domain adaptation approach based on J ST. We verify our proposed approach by conducting experiments on both the movie review data

5 Domain Adaptation using J ST

Given input data cc and a class label 3/, labeled patterns of one domain can be drawn from the joint distribution P(:c, y) = Domain adaptation usually assume that data distribution are different in source and target domains, i.e., Ps(x) 75 Pt(:c).

The task of domain adaptation is to predict the label corresponding to in the target domain.

There has been significant amount of work on algorithms for domain adaptation in NLP.

for domain adaptation where a mixture model is defined to learn differences between domains.

proposed structural correspondence learning (SCL) for domain adaptation in sentiment classification.

We consider a semi-supervised setting for domain adaptation where only unlabeled data is available for the target domain.

As we discussed in the introduction, our goal is to provide a method for domain adaptation based on semi-supervised learning of models with distributed representations.

In this section, we first discuss the shortcomings of domain adaptation with the above-described semi-supervised approach and motivate constraints on inter-domain variability of

Another motivation for the form of regularization we propose originates from theoretical analysis of the domain adaptation problems (Ben-David et al., 2010; Mansour et al., 2009; Blitzer et al., 2007).

One of the most promising research directions on domain adaptation for this setting is based on the idea of inducing a shared feature representation (Blitzer et al., 2006), that is mapping from the initial feature representation to a new representation such that (l) examples from both domains ‘look similar’ and (2) an accurate classifier can be trained in this new representation.

There is a growing body of work on domain adaptation .

Such methods tackle domain adaptation by instance re-weighting (Bickel et al., 2007; Jiang and Zhai, 2007), or, similarly, by feature re-weighting (Sat-pal and Sarawagi, 2007).

Semi-supervised leam-ing with distributed representations and its application to domain adaptation has previously been considered in (Huang and Yates, 2009), but no attempt has been made to address problems specific to the domain-adaptation setting.

In future, we intend to apply the proposed method to other domain adaptation tasks.

This can be considered to be a lower bound that does not perform domain adaptation .

Compared to single-domain sentiment classification, which has been studied extensively in previous work (Pang and Lee, 2008; Tumey, 2002), cross-domain sentiment classification has only recently received attention in response to advances in the area of domain adaptation .

Aue and Gammon (2005) report a number of empirical tests into domain adaptation of sentiment classifiers using an ensemble of classifiers.