From Transformers to Voting Ensembles for Interpretable Sentiment Classification: A Comprehensive Comparison

This study conducts an in-depth investigation of the performance of six transformer models using 12 different datasets—10 with three classes and two with two classes—on sentiment classification. We use these six models and generate all combinations of triple schema ensembles, Majority and Soft vote. In total, we compare 46 classifiers on each dataset and see in one case up to a 7.6% increase in accuracy on a dataset with three classes from an ensemble scheme and, in a second case, up to 8.5% increase in accuracy on a dataset with two classes. Our study contributes to the field of natural language processing by exploring the reasons for the predominance, in this particular task, of Majority vote over Soft vote. The conclusions are drawn after a thorough investigation of the classifiers that are co-compared with each other through reliability charts, analyses of the confidence the models have in their predictions and their metrics, concluding with statistical analyses using the Friedman test and the Nemenyi post-hoc test with useful conclusions.