Bayesian-Boosting Sentiment Classification Algorithm Based on Semantic
Xinxin Li · Journal of Guangxi Normal University · 2010
This paper presents a machine learning algorithm which combines the semantic and statistical aspect for text sentiment classification.First,extract the text of the emotional words as features,and use the statistical methods to get the initial weight,then reweight the feature by analyzing the semantic structure of the text;finally use Bayesian algorithms and Bayesian classification algorithm as a basic classification of the Boosting algorithm to classify the text.The empirical results show that,the accuracy of the Bayesian classification algorithm based on semantic understanding is higher than the Bayesian classification algorithm based on statistic and its accuracy can reach 90%.