Bridging Domains with Words: Opinion Analysis with Matrix Tri-factorizations

With the explosion of user-generated web2.0 content in the form of blogs, wikis and discussion forums, the Internet has rapidly become a massive dynamic repository of public opinion on an unbounded range of topics. A key enabler of opinion extraction and summarization is sentiment classification: the task of automatically identifying whether a given piece of text expresses positive or negative opinion towards a topic of interest. Building high-quality sentiment classifiers using standard text categorization methods is challenging due to the lack of labeled data in a target domain. In this paper, we consider the problem of cross-domain sentiment analysis: can one, for instance, download rated movie reviews from rottentomatoes.com or IMBD discussion forums, learn linguistic expressions and sentiment-laden terms that generally characterize opinionated commentary and then successfully transfer this knowledge to the target domain, thereby building high-quality sentiment models without manual effort? We outline a novel sentiment transfer mechanism based on constrained non-negative matrix tri-factorizations of termdocument matrices in the source and target domains. The constrained matrix factorization framework naturally incorporates document labels via a least squares penalty incurred by a certain linear model and enables direct and explicit knowledge transfer across different domains. We obtain promising empirical results with this approach.