Chinese Sentiment Classification on Imbalanced Data Distribution
Guodong Zhou · Zhongwen xinxi xuebao · 2012
Sentiment classification has undergone significant development in recent years.However,most existing studies assume the balance between the numbers of negative and positive samples,which may not be true in reality.In this paper,we collect product reviews from four domains and find that the positive samples are much more than negative ones.To handle the imbalanced classification in Chinese sentiment classification,we propose a novel approach to combine both sampling and classification algorithms under an ensemble learning framework.Evaluation across different domains shows the proposed approach performs better than several existing imbalanced classification methods.