An empirical approach for opinion detection using significant sentences

Abstract

In this paper we present an unsupervised approach to identify opinion of web users using a set of significant sentences from an opinionated text and to classify web user's opinion into positive or negative. Web users document their opinion in opinionated sites, shopping sites, personal pages etc., to express and share their opinion with other web users. The opinion expressed by web users may be on diverse topics such as politics, sports, products, movies etc. These opinions will be very useful to others such as, leaders of political parties, selection committees of various sports, business analysts and other stake holders of products, directors and producers of movies as well as to the other concerned web users. We use an unsupervised semantic based approach to find users opinion. Our approach first detects subjective phrases and uses these phrases along with semantic orientation score to identify user's opinion from a set of empirically selected significant sentences. Our approach provides better results than the other approaches applied on different data sets. © 2010 Springer-Verlag.