An ensemble method for unbalanced sentiment classification

Experiments on the unbalanced data set prove that this ensemble method can improve the classification performance of unbalanced sentiment data set.

Abstract

Current binary sentiment classification has been focusing on improving the performance of classification, while the imbalance of sentiment data set in practical applications, which means the number of samples in one category is several folds of that of another category, is neglected. Most study on sentiment classification has been done on the balanced data, so these methods perform well on balanced data, while are unable to maintain the same performance on unbalanced data set. This paper proposed a method for unbalanced sentiment classification that combines unbalanced classification method and ensemble learning technique. Both algorithm and data set are considered to enhance the classification performance of imbalance sentiment data set. Under the framework of ensemble learning, this hybrid method integrates three different methods: under-sampling, bootstrap re-sampling and random feature selection to process the data set. Experiments on the unbalanced data set prove that this ensemble method can improve the classification performance of unbalanced sentiment data set.