Using Ensemble Learners to Improve Classifier Performance on Tweet Sentiment Data

Sentiment analysis of tweets requires the ability to reliably and accurately identify the emotional polarity (positive or negative) of instances. This can be challenging, particularly when the data quality is questionable due to noise or imbalance. Ensemble learning algorithms have been shown to offer superior performance compared to non-ensemble techniques in many domains, but have not been thoroughly studied in the domain of tweet sentiment classification. In this work, we compare the performance of two popular ensemble techniques, bagging and boosting. Both bagging and boosting are tested using seven different base learners. Additionally, we compare the performance of ensemble techniques to using each of the base learners with no ensemble technique. Each of the resulting 21 learning algorithms is trained and tested on two datasets, a large automatically labeled lower quality dataset and a small manually labeled high quality dataset. We find that, in general, ensemble learners achieve higher performance on both datasets, and that bagging is superior when data quality is a concern.