Identifying Text Polarity Using Random Walks

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 395–403, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Identifying Text Polarity Using Random Walks Ahmed Hassan University of Michigan Ann Arbor Ann Arbor, Michigan, USA hassanam@umich.edu Dragomir Radev University of Michigan Ann Arbor Ann Arbor, Michigan, USA radev@umich.edu Abstract Automatically identifying the polarity of words is a very important task in Natural Language Processing. It has applications in text classification, text filtering, analysis of product review, analysis of responses to surveys, and mining online discussions. We propose a method for identifying the polarity of words. We apply a Markov ran- dom walk model to a large word related- ness graph, producing a polarity estimate for any given word. A key advantage of the model is its ability to accurately and quickly assign a polarity sign and mag- nitude to any word. The method could be used both in a semi-supervised setting where a training set of labeled words is used, and in an unsupervised setting where a handful of seeds is used to define the two polarity classes. The method is exper- imentally tested using a manually labeled set of positive and negative words. It out- performs the state of the art methods in the semi-supervised setting. The results in the unsupervised setting is comparable to the best reported values. However, the pro- posed method is faster and does not need a large corpus. 1 Introduction Identifying emotions and attitudes from unstruc- tured text is a very important task in Natural Lan- guage Processing. This problem has a variety of possible applications. For example, there has been a great body of work for mining product reputation on the Web (Morinaga et al., 2002; Turney, 2002). Knowing the reputation of a product is very impor- tant for marketing and customer relation manage- ment (Morinaga et al., 2002). Manually handling reviews to identify reputation is a very costly, and time consuming process given the overwhelming amount of reviews on the Web. A list of words with positive/negative polarity is a very valuable resource for such an application. Another interesting application is mining online discussions. A threaded discussion is an electronic discussion in which software tools are used to help individuals post messages and respond to other messages. Threaded discussions include e-mails, e-mail lists, bulletin boards, newsgroups, or Inter- net forums. Threaded discussions act as a very im- portant tool for communication and collaboration in the Web. An enormous number of discussion groups exists on the Web. Millions of users post content to these groups covering pretty much ev- ery possible topic. Tracking participant attitude towards different topics and towards other partici- pants is a very interesting task. For example,Tong (2001) presented the concept of sentiment time- lines. His system classifies discussion posts about movies as either positive or negative. This is used to produce a plot of the number of positive and negative sentiment messages over time. All those applications could benefit much from an automatic …

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