Sentiment Classification Using Sentence-level Lexical Based Semantic Orientation of Online Reviews

Sentiment analysis is the process of extracting knowledge from the peoples’ opinions, appraisals and emotions toward entities, events and their attributes. These opinions greatly impact on customers to make their choices regarding online shopping, choosing events, products and entities. With the rapid growth of online resources, discussion groups, forums and blogs; people communicate through these means of internet on daily basis. As a result, the vast amount of new data in the form of customer reviews and opinions are being generated progressively. So it is desired to develop an efficient and effective sentiment analysis system for online customer reviews and comments. In this study, 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.