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

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 244–252, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP A Non-negative Matrix Tri-factorization Approach to Sentiment Classification with Lexical Prior Knowledge Tao Li Yi Zhang School of Computer Science Florida International University {taoli,yzhan004}@cs.fiu.edu Vikas Sindhwani Mathematical Sciences IBM T.J. Watson Research Center vsindhw@us.ibm.com Abstract 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 possi- ble to perform large-scale mining of pub- lic opinion. Sentiment modeling is thus becoming a critical component of market intelligence and social media technologies that aim to tap into the collective wis- dom of crowds. In this paper, we consider the problem of learning high-quality senti- ment models with minimal manual super- vision. We propose a novel approach to learn from lexical prior knowledge in the form of domain-independent sentiment- laden terms, in conjunction with domain- dependent unlabeled data and a few la- beled 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 studies demon- strate the effectiveness of our approach on a variety of real-world sentiment predic- tion tasks. 1 Introduction Web 2.0 platforms such as blogs, discussion fo- rums and other such social media have now given a public voice to every consumer. Recent sur- veys have estimated that a massive number of in- ternet users turn to such forums to collect rec- ommendations for products and services, guid- ing their own choices and decisions by the opin- ions that other consumers have publically ex- pressed. Gleaning insights by monitoring and an- alyzing large amounts of such user-generated data is thus becoming a key competitive differentia- tor for many companies. While tracking brand perceptions in traditional media is hardly a new challenge, handling the unprecedented scale of unstructured user-generated web content requires new methodologies. These methodologies are likely to be rooted in natural language processing and machine learning techniques. Automatically classifying the sentiment ex- pressed in a blog around selected topics of interest is a canonical machine learning task in this dis- cussion. A standard approach would be to manu- ally label documents with their sentiment orienta- tion and then apply off-the-shelf text classification techniques. However, sentiment is often conveyed with subtle linguistic mechanisms such as the use of sarcasm and highly domain-specific contextual cues. This makes manual annotation of sentiment time consuming and error-prone, presenting a bot- tleneck in learning high quality models. Moreover, products and services of current focus, and asso- ciated community of bloggers with their idiosyn- cratic expressions, may rapidly evolve over time causing models to potentially lose performance and become stale. This motivates the problem of learning robust sentiment models from minimal supervision. In their seminal work, (Pang et al., 2002) demonstrated …

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