Chinese Micro-blog Sentiment Analysis Based on SVM and Complex Phrasing

Text sentiment analysis technology is a hot topic recently. As a short text, there is a feature of using complex sentences to express the author's true views and complex emotional tendencies in micro-blogs. In current researches on sentiment classification based on machine learning, few of them focus on complex sentences. This paper proposed a sentiment analysis method based on SVM and complex phrasing classifier, and made a full analysis of structural features of Chinese conditional sentences, transition sentences and multiple negative sentences, which were taken as text features. A variety of different combinations of features were chosen, including emotional words, speech, negative words, the degree of adverbs and punctuation, etc., to optimize the results of sentiment analysis through multiple sets of experiments. The experiments show that when we choose the combinations of features of emotional words, part of speech and complex sentence patterns, this method improved the accuracy of sentiment classification compared to the common method.