Enhancing Ensemble Learners with Data Sampling on High-Dimensional Imbalanced Tweet Sentiment Data

High dimensionality and class imbalance are two important concerns when training tweet sentiment classifiers. Feature selection techniques reduce dimensionality by selecting an optimal subset of features. Class imbalance can be addressed by either using classifiers that are robust to the impact of class imbalance, such as those trained with an ensemble learning technique, or by using data sampling techniques to create a sampled training set with a more balanced class ratio. These separate techniques can be combined together to address both class imbalance and high-dimensionality; however, it is unclear if it is necessary to use data sampling and ensemble techniques together as both are used to target class imbalance. In our study, we investigate if the addition of random undersampling to Select-Boost (feature selection and boosting) significantly improves the performance of sentiment classifiers trained on imbalanced tweet data. We evaluate classifiers trained using four base learners and three feature subset sizes across two highly dimensional imbalanced datasets. Our results show, for tweet sentiment, the inclusion of random undersampling significantly improves classification performance and indicates this may be more noticeable on datasets with greater levels of class imbalance.