tinaal at S em E val-2025 Task 11: Enhancing Perceived Emotion Intensity Prediction with Boosting Fine-Tuned Transformers (2025)

Abstract: This paper presents a framework for perceived emotion intensity prediction, focusing on SemEval-2025 Task 11 Track B. The task involves predicting the intensity of five perceived emotions—anger, fear, joy, sadness, and surprise—on an ordinal scale from 0 (no emotion) to 3 (high emotion). Our approach builds upon our method introduced in the WASSA workshop and enhances it by integrating ModernBERT in place of the traditional BERT model within a boosting-based ensemble framework. To address the difficulty in capturing fine-grained emotional distinctions, we incorporate class-preserving mixup data augmentation, a custom Pearson CombinLoss function, and fine-tuned transformer models, including ModernBERT, RoBERTa, and DeBERTa. Compared to individual fine-tuned transformer models (BERT, RoBERTa, DeBERTa and ModernBERT) without augmentation or ensemble learning, our approach demonstrates significant improvements. The proposed system achieves an average Pearson correlation coefficient of 0.768 on the test set, outperforming the best individual baseline model. In particular, the model performs best for sadness (r = 0.808) and surprise (r = 0.770), highlighting its ability to capture subtle intensity variations in the text. Despite these improvements, challenges such as data imbalance, performance on low-resource emotions (e.g., anger and fear), and the need for refined data augmentation techniques remain open for future research.

## introduction emotions play a critical role in human communication, influencing decision-making, relationships, and interactions. in recent years, automatic detection and modelling of emotions in the text have attracted significant attention within the natural language processing (nlp) community due to their potential applications in areas such as mental health support, and personalized recommender systems @xcite . despite this progress, emotion recognition remains challenging because of the complexity and subjectivity inherent in emotional expression. this study focuses on the detection of perceived emotions, which predicts the emotions that most people would associate with a given text. perceived emotions are influenced by culture and individual differences, making their detection complex and nuanced @xcite . previous studies, such as those of @xcite , have highlighted the importance of perceived emotions in tasks like sentiment analysis and emotion intensity prediction. in this work, we explore semeval-2025 task 11 track b: emotion intensity prediction of the shared task on knowledge representation and reasoning @xcite . track b aims to predict the intensity of perceived emotions, joy, sadness, fear, anger, surprise, or disgust, on an ordinal scale ranging from 0 (no emotion) to 3 (high emotion). previous research has explored techniques such as ordinal regression @xcite and multi-task learning @xcite to better model emotion intensity. however, there remains a gap in optimizing these models specifically for emotion intensity prediction, particularly in learning from limited data, preserving ordinal relationships between intensity levels, and integrating augmentation techniques that improve generalization without distorting emotional meaning. to address these challenges, this study introduces a novel framework that builds on our method proposed in the wassa workshop, enhancing it with modernbert as a replacement for the traditional bert model. the proposed approach integrates class-preserving mixup data augmentation, a custom pearson combinloss function, and a boosting-based ensemble strategy to improve the model's ability to handle the ordinal nature of intensity labels, capture subtle emotional variations, and enhance robustness across different contexts. the ensemble leverages multiple fine-tuned transformer models, including modernbert, roberta, and deberta, to combine their strengths and improve generalization. through several evaluations on semeval-2025 task 11 track b, we demonstrate that our method significantly outperforms individual transformer-based models, achieving improved pearson correlation scores, particularly for sadness and surprise. ## methodology our approach enhances emotion intensity prediction by integrating modernbert into a boostingbased ensemble framework, based on the method introduced in the wassa workshop @xcite . figure 1 illustrates our method for track b in semeval-2025 task 11. it consists of data augmentation, the pearson combinloss function, fine-tuned modernbert, deberta or roberta models, and the effective boosting strategy. in the following, we describe each component in detail. ## conclusions this paper presents a novel framework for perceived emotion intensity prediction, developed for semeval-2025 task 11 track b, by integrating modernbert within a boosting-based ensemble model. the proposed approach builds upon the method introduced in the wassa workshop and incorporates class-preserving mixup data augmentation, a custom pearson combinloss function, and fine-tuned transformer models to address key challenges such as the ordinal nature of emotion intensity labels, and capturing fine-grained emotional distinctions. our system achieved an average pearson correlation coefficient of 0.768 on the test set, demonstrating significant improvements over baseline models. the ensemble approach, leveraging modernbert, roberta, and deberta, was particularly effective in modeling subtle variations in sadness and surprise, achieving pearson correlations of 0.808 and 0.770, respectively. however, challenges remain in accurately modeling fear and anger, likely due to limited training samples and inherent subjectivity in emotional expression. future work could explore data augmentation strategies, adaptive loss functions tailored to ordinal emotion scales, and context-aware transformer models to enhance emotion intensity prediction further. additionally, integrating multimodal signals such as prosody and speech patterns could help improve robustness in real-world applications, particularly in mental health support and personalized conversational ai.