NLP - DU at S em E val-2025 Task 11: Analyzing Multi-label Emotion Detection (2025)

Abstract: This paper describes NLP-DU’s entry to SemEval-2025 Task 11 on multi-label emotion detection. We investigated the efficacy of transformer-based models and propose an ensemble approach that combines multiple models. Our experiments demonstrate that the ensemble outperforms individual models under the dataset constraints, yielding superior performance on key evaluation metrics. These findings underscore the potential of ensemble techniques in enhancing multi-label emotion detection and contribute to the broader understanding of emotion analysis in natural language processing.

## introduction emotion detection seeks to identify and categorize emotions conveyed in textual data. this task presents significant challenges due to the inherently complex and overlapping nature of human emotions, as well as the difficulty associated with acquiring high-quality labeled datasets. @xcite introduce the brighter dataset, which aims to bridge the gaps in textual emotion recognition across 28 languages and further enriched their semantic evaluation work in @xcite . furthermore @xcite introduce evaluating the capabilities of large language models for multi-label emotion understanding. recent research by @xcite underscores the necessity of modeling both label dependence and modality dependence in multi-modal, multi-label emotion detection, further highlighting the intricacies involved in this domain. in our approach, we first explored cnn and lstm-based solutions and checked for baseline performance after training. then we chose to explore transformer-based models for multi-label emotion detection. we experimented with mod-ernbert @xcite , @xcite , @xcite , xlm-roberta @xcite , @xcite , and xlnet @xcite , some state-of-the-art transformer models known for their strong contextual understanding and generalization capabilities. to address the challenges posed by a compact dataset @xcite with sensitive labeling and emotion context added to each sentence, we employed data augmentation techniques and leveraged multiple dataset splitting techniques such as balanced stratified k-fold splitting, tree-based splitting, k-means splitting, and balanced stratified splitting for robust evaluation. additionally, we propose an ensemble approach that combines multiple transformer-based models, for example, modernbert and deberta, via weighted averaging, improving overall performance. to encourage reproducibility, we have released our code and models, which can be accessed at: https://github.com/ssadman887/semevaltask-2025 . ## experimental results and analysis we evaluated both individual transformer-based models and ensemble strategies, using the development (dev) and test datasets. performance is assessed through f1 scores (macro and micro) and accuracy, providing a comprehensive view of model effectiveness in this multi-label setting. ## conclusion we employed various data splitting techniques and augmentation strategies to enhance the robustness of our training process. one of the key challenges we encountered was the inherent imbalance in the dataset, with certain emotions being overrepresented. additionally, we identified instances of mislabeling within the training data, which introduced noise into the learning process. despite these challenges, our approach enabled us to achieve a competitive f1 score, demonstrating the effectiveness of our data handling strategies and model optimization techniques.