In this paper, we present a dependency treebased method for sentiment classification of Japanese and English subjective sentences using conditional random fields with hidden variables. Subjective sentences often contain words which reverse the sentiment polarities of other words. Therefore, interactions between words need to be considered in sentiment classification, which is difficult to be handled with simple bag-of-words approaches, and the syntactic dependency structures of subjective sentences are exploited in our method. In the method, the sentiment polarity of each dependency subtree in a sentence, which is not observable in training data, is represented by a hidden variable. The polarity of the whole sentence is calculated in consideration of interactions between the hidden variables. Sum-product belief propagation is used for inference. Experimental results of sentiment classification for Japanese and English subjective sentences showed that the method performs better than other methods based on bag-of-features.
A method of sentiment classification for Japanese and English sentences was proposed in REF , which was based on conditional random fields (CRF) with hidden variables and dependency tree to compute the polarity of the entire sentence, where the cooperation of the hidden variables was considered.
Dependency Tree-based Sentiment Classification using CRFs with Hidden Variables
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Computer Science