Paper: Identifying Text Polarity Using Random Walks

ACL ID P10-1041
Title Identifying Text Polarity Using Random Walks
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

Automatically identifying the polarity of words is a very important task in Natural Language Processing. It has applications intextclassification, textfiltering, 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 te...