Intelligent Aspect based Model for Efficient Sentiment Analysis of User Reviews

Abstract

Mining online reviews to extract opinion targets and words are substantial tasks in fine-grained opinion mining. The main aim is to mine sensible multi-grain aspects and opinion words from unlabeled reviews. In this paper, Combined Aspect based Sentiment Model (CASM) is propounded to cooperatively mine multi-grain features and opinions. CASM deals with aspects, opinions, sentiment polarity and granularity concurrently. Support Vector Machine-Radial Basis Function kernel (SVM-RBF) classifier is applied to improve CASM by splitting aspects and opinion words. CASM-SVM-RBF deals with two different kinds of aspects and opinions: general and particular aspects, overall opinions and aspect-precise opinions. Candidates with improved confidence are mined as opinion targets or words.

Index Terms:Aspects, Opinions, Sentiment, Support Vector Machine, Radial Basis Function, Support Vector Machine (SVM), Radial Basis Function (RBF)