Chinese microblog sentiment analysis based on semi-supervised learning

This paper adopts a semi-supervised method which is based on bootstrapping to analyze Sina microblog data which size is about 269 M. The Support Vector Machine (SVM) method is used in subjective and objective classification and polarity classification. Our method can extend the size of seed samples by learning automatically with a small size of labeled corpus. It can improve the ability of sentiment classification of SVM by using the iteration method. A weighted factor to control the weight of new seed samples during the following training process can improve classification performance. The experiment results show that sentiment analysis of Chinese microblog based on bootstrapping not only saves much time of manual annotation but also can get better performance. The results of subjective and objective classification achieve the best accuracy rate of 62.9%, and the best accuracy rate of sentiment polarity classification is 57%.