Microblog sentiment classification with heterogeneous sentiment knowledge

Microblogging services, such as Twitter, are very popular for information release and dissemination. Analyzing the sentiments in massive microblog messages is useful for sensing the public's opinions on various topics, which has wide applications in both academic and industrial fields. However, microblog sentiment analysis is a challenging task, because microblog messages are short and noisy, and contain massive user-invented acronyms and informal words. It is expensive and time-consuming to manually annotate sufficient samples for training an accurate and robust microblog sentiment classifier. Fortunately, unlabeled microblog messages can provide a lot of useful sentiment knowledge. For example, emoticons are frequently used in microblog messages and they usually indicate sentiment orientations. In this paper, we propose to extract useful sentiment knowledge from massive unlabeled messages to enhance microblog sentiment classification. Three kinds of sentiment knowledge, i.e., contextual similarity knowledge, word-sentiment knowledge, and contextual polarity knowledge, are explored. We propose a unified framework to incorporate the heterogenous sentiment knowledge into the learning of microblog sentiment classifiers. An efficient optimization method based on ADMM is introduced to solve the model of our framework and an accelerated algorithm is proposed to tackle the most time-consuming step. Extensive experiments were conducted on three benchmark Twitter datasets. The experimental results show that our approach can improve the performance of microblog sentiment classification effectively and efficiently.