Our goal now is to bias these models with constraints incorporating (a) labels of features (coming from a domain-independent sentiment lexicon ), and (b) labels of documents for the purposes of domain-specific adaptation.

Our interest in the first set of experiments is to explore the benefits of incorporating a sentiment lexicon over unsupervised approaches.

These methods do not make use of the sentiment lexicon .

Size of Sentiment Lexicon We also investigate the effects of the size of the sentiment lexicon on the performance of our model.

We used a sentiment lexicon generated by the IBM India Research Labs that was developed for other text mining applications (Ramakrishnan et al., 2003).

Treated as a set of labeled features, the sentiment lexicon is incorporated as one set of constraints that enforce domain-independent prior knowledge.

So far our models have made no demands on human effort, other than unsupervised collection of the term-document matrix and a onetime effort in compiling a domain-independent sentiment lexicon .

Such work generally exploits textual features for fact-based analysis tasks or lexical indicators from a sentiment lexicon .

Accordingly, much research has focused on recognizing terms’ semantic orientations and strength, and compiling sentiment lexicons (Hatzivassiloglou and Mckeown, 1997; Turney and Littman, 2003; Kamps et al., 2004; Whitelaw et al., 2005; Esuli and Sebastiani, 2006).

The goal of this paper is not to create or choose an appropriate sentiment lexicon , but rather it is to discover useful term features other than the sentiment properties.

For this reason, one sentiment lexicon , namely SentiWordNet, is utilized throughout the whole experiment.

SentiWordNet is an automatically generated sentiment lexicon using a semi-supervised method (Esuli and Sebastiani, 2006).