A Non-negative Matrix Tri-factorization Approach to Sentiment Classification with Lexical Prior Knowledge

Sentiment classification refers to the task of automatically identifying whether a given piece of text expresses positive or negative opinion towards a subject at hand. The proliferation of user-generated web content such as blogs, discussion forums and online review sites has made it possible to perform large-scale mining of public opinion. Sentiment modeling is thus becoming a critical component of market intelligence and social media technologies that aim to tap into the collective wisdom of crowds. In this paper, we consider the problem of learning high-quality sentiment models with minimal manual supervision. We propose a novel approach to learn from lexical prior knowledge in the form of domain-independent sentimentladen terms, in conjunction with domaindependent unlabeled data and a few labeled documents. Our model is based on a constrained non-negative tri-factorization of the term-document matrix which can be implemented using simple update rules. Extensive experimental studi...

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Tao Li, Yi Zhang 0005, Vikas Sindhwani
• Keywords: ACL 2009, Computational Linguistics, Constrained Non-negative Tri-factorization, Real-world Sentiment Prediction, User-generated Web Content