@inproceedings{awadallah2010identifying,
author = {Awadallah, Ahmed and Radev, Dragomir},
title = {Identifying Text Polarity Using Random Walks},
booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics},
year = {2010},
month = {July},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/identifying-text-polarity-using-random-walks/},
pages = {395-403},
edition = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics},
}