Sentiment Classification for Chinese Netnews Comments Based on Multiple Classifiers Integration
With the development of World Wide Web technologies, more and more netizens express their opinions on society and politics in netnews comments. Sentiment classification is one of the most important subproblems of opinion mining, which can classify netnews comments as positive or negative to help government automatically identify the netizens viewpoints on news event and make right decision or help enterprises find out weather the customers satisfy the products or not Most of the researches for sentiment classification only use single classifier, such as kNN, NaTve Bayes and Support Vector Machine (SVM). In this paper, we use two multiple classifiers integration algorithms, which are Bagging and Boosting, to conduct the sentiment classification. Different feature selection methods are also investigated. The result of experiment shows that AdaBoost approach, a type of Boosting, usually achieve better performance than Bagging and single classifier and feature selection based on statistic is better than POS-based method for sentiment classification of Chinese netnews comments.
netnews comments multiple classifiers integration Bagging Boosting
Wen Fan Shutao Sun Guohui Song
School of Computer Science, Communication University of China Beijing, P.R.China
国际会议
昆明、丽江
英文
829-834
2011-04-15（万方平台首次上网日期，不代表论文的发表时间）