Fine-grained sentiment classification based on HowNet

With the rapid development of Web 2.0, sentiment classification -- the task of automatically classifying reviews as positive or negative has received widely attention in the past few years. However, most existing researches focus on mining the polarity of a text as a whole. In this paper, a HowNet based fine-grained sentiment classification algorithm is proposed, which address the problem at sentence level. First, HowNet tool is employed to extract the topic of each sentence by computing the similarity to a set of reference concepts. Second, supervised and unsupervised sentiment analysis methods is applied to determine its orientation. Finally, fine-grained orientation of the review set will be identified by sum up the sentiment of different topis aspect. Experiments on Chinese real hotel review dataset collected from Ctrip.com shows that the proposed algorithm obtain satisfied classification result.