MRS at S em E val-2025 Task 11: A Hybrid Approach for Bridging the Gap in Text-Based Emotion Detection (2025)

Abstract: We tackle the challenge of multi-label emotion detection in short texts, focusing on SemEval-2025 Task 11 Track A. Our approach, RoEmo, combines generative and discriminative models in an ensemble strategy to classify texts into five emotions: anger, fear, joy, sadness, and surprise.The generative model, instruction-finetuned on emotion detection datasets, undergoes additional fine-tuning on the SemEval-2025 Task 11 Track A dataset to enhance its performance for this specific task. Meanwhile, the discriminative model, based on binary classification, offers a straightforward yet effective approach to classification.We review recent advancements in multi-label emotion detection and analyze the task dataset. Our results show that RoEmo ranks among the top-performing systems, demonstrating high accuracy and reliability.

## introduction emotion detection plays a critical role in natural language processing (nlp), yet remains a challenging task due to the nuanced and often subjective nature of emotional content. while recent advancements in emotion detection have demonstrated significant progress, there is still a need to enhance both the accuracy and efficiency of these models @xcite . emotion detection in short text settings presents a significant challenge due to the limited contextual information available, which constrains traditional nlp models that typically rely on richer textual input @xcite . the scarcity of extensive context necessitates the development of specialized approaches capable of accurately capturing emotional content from minimal linguistic cues. this challenge becomes even more pronounced in multilabel emotion detection, where the task involves identifying the emotion that most people perceive in the speaker's words-rather than the reader's m odel ? sadness ? fear ? anger ? joy ? sur pr ise 'i found out six weeks befor e the wedding that my dad had only six weeks to live (he had cancer for two year s...a fact she was fully awar e of).' figure 1 : the challenge of multi-label emotion detection. this task focuses on identifying the emotion that most people perceive in the speaker's words, rather than the reader's reaction, the emotion of other characters, or the speaker's true feelings. the difficulty arises from the subjective nature of language interpretation and the inherent ambiguity in emotional expression. our study centers on semeval-2025 task 11 @xcite , a benchmark challenge in emotion detection for short texts. specifically, we address track a, which requires classifying texts into one or more of five emotions: anger, fear, joy, sadness, and surprise. while track a includes multiple languages, our focus is exclusively on english. our approach, roemo, combines the predictions of roberta-large @xcite and emot5 @xcite to enhance emotion detection in short texts. ## related works emotion detection in text is a long-studied task in natural language processing with several approaches achieving success over the years. in this section, we summarize the most common emotion models employed in emotion detection and recent successful strategies for emotion detection. ## approach in this work, we employ two transformer-based models, roberta-large and emot5, for emotion detection. our approach explores both discriminative and generative modeling paradigms to classify emotions effectively. both models are finetuned for 15 epochs using the adamw optimizer with a learning rate of 2 × 10 -5 on an nvidia rtx a6000 gpu. ## experiments in this section, we analyze the dataset to understand its characteristics and challenges. we then present the performance of roberta-large, followed by emot5, and finally, evaluate the combined output using our ensemble method, roemo. we highlight the benefits of this hybrid approach in improving multi-label emotion detection. figure 3 : the distribution of emotion labels in the training data. note that some emotions can co-occur-for example, a single text may be labeled with both joy and surprise. ## conclusion in this work, we tackled multi-label emotion detection in short texts by leveraging a hybrid approach that integrates both generative and discriminative models. by combining the instruction finetuned generative model with the of a discriminative model, our method effectively captures diverse emotional expressions while maintaining computational efficiency. through an ensemble strategy, we demonstrated that merging predictions from both models enhances classification performance. our analysis of recent developments in the field, along with empirical results on semeval-2025 task 11, highlights the effectiveness of our approach. the findings suggest that this hybrid framework is a promising direction for improving emotion detection systems, and balancing generalization and efficiency.