Detecting Opinion Polarities Using Ensemble of Classification Algorithms

Abstract

Text classification enables developers to track consumer’s reaction to e-commerce products. Such information, often expressed in the form of raw emotions, can be used to measure consumers’ emotions and their emotional preference for commodities, so as to help future consumers to make choices. However, capturing and interpreting human emotions expressed in product reviews is not a trivial task. Challenges stem from integrated approach and optimal feature combination methods of different classifiers. In this paper, we present a ensemble framework of text classification. It can found that the most adapitve feature sets for classifiers. An effective method of CRF model to process medium and long text is proposed, this method significantly improves the CRF model’s ability to handle text, length of which is more than ten. As base learning algorithms, three classifiers are integrated to improve the efficiency of classification, which are Support Vector Machine (SVM), Conditional Random Filed(CRF), and Naive Bayes Multinomial(NBM). The experiments prove the effectiveness of our proposed method.