Multi-Task Learning Transformers: Comparative Analysis for Emotion Classification and Intensity Prediction in Social Media
Qasid Labeed, Xing Liang · 2024
In the realm of sentiment analysis, transformer models have revolutionised sentiment classification tasks driven by their exponential growth in pre-training parameters, significantly enhancing performance. However, this study aims to push the boundaries further by introducing a novel framework for a multi-task learning transformer model1with a dual learning objective: conducting emotion classification on tweets while concurrently detecting their intensity. Various pre-trained transformer models, including BERT, RoBERTa, and DistillBERT are employed to evaluate the effectiveness of this framework using the WASSA 2017 EmoInt dataset. Given its distinctive approach, this multi-task learning study identifies no direct competitors pursuing a similar methodology on the same dataset, prompting a comparative analysis among these transformer models for multi-task learning on the WASSA 2017 EmoInt dataset. The results showcase an impressive F1 score of 0.864 for emotion classification, while emotion intensity prediction achieves a Pearson correlation of 0.705 (R=0.705), closely aligning with the best results in the WASSA 2017 competition. Additionally, a supplementary investigation was conducted focusing solely on single-task learning for emotion classification and emotion intensity prediction, respectively. Comparing the performance of multi-task learning to single-task learning demonstrates the effectiveness and accuracy of the proposed multi-task learning framework. Furthermore, it yields a Pearson correlation of 0.779 (R=0.779) in emotion intensity prediction, surpassing the state-of-the-art baseline method for sentiment analysis.