A Random Walk based Model for Identifying Semantic Orientation

Abstract

Automatically identifying the polarity of words is a very importanttask in Natural Language Processing. It has applications intext classification, text filtering, analysis of product review,analysis of responses to surveys, and mining on-line 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 quicklyassign a polarity sign to any word.The method could be used both in a semi-supervised setting where atraining set of labeled words is used, and in an unsupervised settingwhere a handful of seeds is used to define the two polarity classes.We also propose extensions of our method for identifying the polarity of foreign words and out-of-vocabulary words.The method is experimentally tested using manuallylabeled sets 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.