Sentiment classification from online customer reviews using lexical contextual sentence structure

Abstract

Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers, ease in making choices regards to online shopping, choosing events, products, entities, etc. When an important decision needs to be made, consumers usually want to know the opinion, sentiment and emotion of others. With rapidly growing online resources such as online discussion groups, forums and blogs, people are commentating via the Internet. As a result, a vast amount of new data in the form of customer reviews, comments and opinions about products, events and entities are being generated more and more. So it is desired to develop an efficient and effective sentiment analysis system for online customer reviews and comments. In this paper, the rule based domain independent sentiment analysis method is proposed. The proposed method classifies subjective and objective sentences from reviews and blog comments. The semantic score of subjective sentences is extracted from SentiWordNet to calculate their polarity as positive, negative or neutral based on the contextual sentence structure. The results show the effectiveness of the proposed method and it outperforms the word level and machine learning methods. The proposed method achieves an accuracy of 97.8% at the feedback level and 86.6% at the sentence level. © 2011 Springer-Verlag.