Sentiment Analysis With The Exploration Of Overall Opinion Sentences

With the rapid growth of opinionated contents, e.g. product reviews, sentiment analysis has drawn much attention from the researchers. The most fundamental task of sentiment analysis is document sentiment classification which aims to predict the overall sentiment (e.g. positive or negative) towards the opinion target in a review. There are usually various opinion sentences towards different aspects with different sentiments. Among them, the overall opinion towards the whole target should be more deterministic in document sentiment prediction. However, most existing methods treat all the sentences equally, thus, they may encounter difficulty especially when the sentiments of most aspect opinion sentences differ from the overall sentiment. To address this, we propose a novel method for document sentiment classification which adequately explores the effect of overall opinion sentences. The method is extended from structural SVM, and the overall opinion sentences are taken as the hidden variables for document sentiment. Experiments on several standard product review datasets show the effectiveness of our method.