Chinese microblog sentiment analysis based on sentiment features

Abstract

As the microblog has increasingly become an information platform for netizens to share their ideas, the study on the sentiment analysis of microblog has got scholars’ wide attention both at home and abroad. The primary goal of this research is to improve the accuracy of microblog sentiment polarity classification. With a view to the characteristics of microblog, a new method of semantically related feature extraction is proposed. Firstly, the Chinese word features are selected by text presentation in VSM and computing the weight by TF*IDF. Secondly, the proposed eight microblog semantic features are extracted, including sentence sentiment judgment based on emotional dictionary. Finally, three kinds of machine learning methods are used to classify the Chinese microblog under the feature vector combining the two methods. The experimental results indicate that the proposed feature extraction method outperforms the state-of-the-art approaches, and for this feature extraction algorithm, the classification performance is best when using the Naïve Bayes algorithm.