Structured microblog sentiment classification via social context regularization
Microblog sentiment analysis is a fundamental problem for many interesting applications. Existing microblog sentiment classification methods judge the sentiment polarity mainly according to textual content. However, since microblog messages are very short and noisy, and their sentiment polarities are often ambiguous and context dependent, the accuracy of microblog sentiment classification is usually unsatisfactory. Fortunately, microblog messages lie in social media and contain rich social contexts. The social context information often implies sentiment connections between microblog messages. For example, a microblogging user usually expresses the same sentiment when posting multiple messages towards the same topic. Motivated by these observations, in this paper we propose a structured microblog sentiment classification (SMSC) framework. Our framework can combine social context information with textual content information to improve microblog sentiment classification accuracy. Two kinds of social contexts are used in our framework, i.e. social connections between microblog messages brought by the same author and social connections brought by social relations between users. In our framework, social context information is formulated as the graph structure over the sentiments of microblog messages. The objective function of our framework is a tradeoff between the agreement with content based sentiment predictions and the consistency with social contexts. An efficient optimization algorithm is introduced to solve our framework. Experimental results on two Twitter sentiment analysis benchmark datasets indicate that our method can outperform baseline methods consistently and significantly.
Neurocomputing