Research on Semantic Orientation Analysis for Topics in Chinese Sentences

This paper presents how to identify the topics as well as the relations bewteen the topics and the sentimental descriptive terms in a Chinese sentence,and how to compute the sentiment orientation(polarity) of the topics.We extract the topics and their attributes from a sentence with the help of a domain ontology,then identify the relations between the topics and sentimental descriptive terms based on parsing results,and finally determine the polarity of each topic in the sentence.The experiment has shown that the F-measure of the improved SBV polarity transfer algorithm for identifying topics and the polarity reaches 72.41% as compared with the manual annotation corpus which serves as a golden standard.It is increased by 7.6% and 2.09% than the F-measure of the original SBV and VOB polarity transfer algorithm respectively.Therefore,the proposed improved SBV polarity transfer algorithm is reasonable and effective.