Imbalanced Sentiment Classification with Multi-strategy Ensemble Learning

Recently, sentiment classification has become a hot research topic in natural language processing. But most existing studies assume that the samples in the negative and positive categories are balanced, which might not be true in real applications. In this paper, we investigate sentiment classification tasks where the class distribution of the sam-ples is imbalanced. To handle the imbalanced problem, we propose a multi-strategy ensemble learning approach to this problem. Our ensemble approach integrates sample-ensemble, feature-ensemble, and classifier-ensemble by ex-ploiting multiple classification algorithms. Evaluation across four domains shows that our ensemble approach outper-forms many other popular approaches that handling imbal-anced classification problems, such as re-sampling and cost-sensitive approaches, and is proven effective for imbalanced sentiment classification.