Recognizing Emotions from Texts Using an Ensemble of Transformer-Based Language Models

The use of ensembles has given rise to improved performance in various machine learning tasks. Following the performance of major transformer-based language models in detecting emotions from written texts, the paper investigates the ensemble's performance of the RoBERTa and XLNet transformer-based language models in recognizing emotions from the ISEAR dataset. Finally, the results obtained outperformed the F1-scores of current works in literature with a higher F1-score of 0.75 in detecting emotions from the ISEAR text data.