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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 random walk model to a large word relatedness 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 magnitude to any word. . | 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 random walk model to a large word relatedness 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 magnitude 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 experimentally tested using a manually labeled set of positive and negative words. It outperforms 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 proposed method is faster and does not need a large corpus. 1 Introduction Identifying emotions and attitudes from unstructured text is a very important task in Natural Language 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 important for marketing and customer relation management 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. .
