Word sentiment polarity disambiguition based on opinion level context

Many opinion keywords carry different polarities when they are used in different contexts, posing huge challenges to opinion mining research. To address the word sentiment polarity disambiguation (WSPD) task, the opinion level context information is studies in this paper, and an effective method is designed to make good use of the context information to resolve the sentiment polarity ambiguity. Different from the traditional way that considers surrounding n-grams, we specially consider the associated opinion target, modifying constituents and conjunctions as context of a given sentiment keyword. To locate the context information precisely, we make use of dependency relation between words. We then devise a statistical equation to calculate probability that the given keyword carries certain sentiment polarity. Preliminary results show that the method yields encouraging accuracy.