Probabilistic Language Modelling for Context-Sensitive Opinion Mining

Existing opinion mining methods often utilize a static lexicon-based approach or a supervised machine learning approach to identify sentiment indicators from texts. Nevertheless, the former method often fails to identify context-sensitive semantics of the opinion indicators, and the latter approach requires a large number of human labeled training examples. The main contribution of this paper is the development of a novel opinion mining method underpinned by context-sensitive text mining and probabilistic language modeling method to improve the effectiveness of opinion mining. Our initial experiments show that the proposed the opinion mining method outperforms the purely lexicon-based method in terms of several benchmark measures. The practical implication of our research work is that business managers can apply our methodology to more effectively extract business analytics from user-contributed contents in the Social Web.