A multi-label classification based approach for sentiment classification

A detailed empirical study of different multi-label classification methods on sentiment classification is conducted to compare their classification performances. Specifically, total 11 state of the art multi-label classification methods are compared on two microblog datasets and 8 evaluation metrics are used. The effects of the three sentiment dictionaries for multi-label classification are empirically studied and compared, which, to the best of our knowledge, have not been performed. The performed empirical comparisons show that Dalian University of Technology Sentiment Dictionary has the best performance among the three different sentiment dictionaries.