Habib University at S em E val-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection (2025)

Abstract: Emotion detection in text has emerged as a pivotal challenge in Natural Language Processing (NLP), particularly in multilingual and cross-lingual contexts. This paper presents our participation in SemEval 2025 Task 11, focusing on three subtasks: Multi-label Emotion Detection, Emotion Intensity Prediction, and Cross-lingual Emotion Detection. Leveraging state-of-the-art transformer models such as BERT and XLM-RoBERTa, we implemented baseline models and ensemble techniques to enhance predictive accuracy. Additionally, innovative approaches like data augmentation and translation-based cross-lingual emotion detection were used to address linguistic and class imbalances. Our results demonstrated significant improvements in F1 scores and Pearson correlations, showcasing the effectiveness of ensemble learning and transformer-based architectures in emotion recognition. This work advances the field by providing robust methods for emotion detection, particularly in low-resource and multilingual settings.

## introduction emotion detection in text has become an essential task in natural language processing (nlp), particularly with the rapid growth of digital communication and social media. identifying emotions accurately in textual data can enhance applications such as mental health monitoring, customer service, and user sentiment analysis. however, this task is complicated by the subtlety and complexity of emotional expression across languages and cultures. while traditional machine learning methods have provided baseline solutions, recent advances in deep learning and transformer models, like bert, have shown promise in improving emotion recognition capabilities. this research builds on this foundation by focusing on the challenges presented in semeval task * corresponding author 11. our work addresses three primary subtasks as defined in semeval 2025 task 11: (1) multi-label emotion detection (track a), which involves detecting multiple emotions such as joy, sadness, fear, anger, and surprise from text snippets; (2) emotion intensity prediction (track b), where the goal is to predict the intensity of each emotion on an ordinal scale from 0 (none) to 3 (high); and ## related works recent work in emotion recognition has tackled several interrelated challenges. in the domain of emotion recognition in conversations (erc), tasks such as semeval 2024 task 10 introduced the emotion discovery and reasoning its flip in conversation (ediref) challenge @xcite , which explored recognizing emotion transitions in both english and code-mixed hindi-english dialogues. approaches by teams like umuteam @xcite demonstrated the effectiveness of fine-tuning pre-trained transformers (e.g., bert) on annotated conversational datasets despite challenges such as subtle emotional shifts. parallel efforts have focused on cross-lingual emotion detection, where researchers have leveraged multilingual models like mbert and xlm-roberta to predict emotions across languages @xcite . models such as those proposed by @xcite have shown state-of-the-art performance in code-mixed texts, reinforcing the importance of multilingual training for tasks like task 11. further, emotional flip reasoning (efr)the process of identifying trigger utterances responsible for emotional shifts -has been investigated to enhance conversational agents' empathy and contextual understanding @xcite . ensemble approaches, as seen in masontigers' work on semantic textual relatedness @xcite , also indicate that combining multiple models can lead to more robust performance. additional datasets such as goemotions @xcite and semeval 2018 task 1 @xcite have contributed fine-grained emotion intensity labels, providing further context to our investigations. ## experimental setup our experiments were conducted separately for each track, leveraging transformer-based models and ensemble learning strategies. we utilized the provided training and validation sets, with the validation set incorporated into training for the final submission. preprocessing, hyperparameter tuning, and ensemble strategies varied across tasks. ## conclusion our work demonstrates that transformer-based models, particularly when combined in ensemble frameworks, can effectively address the challenges of emotion detection in text. for both multi-label classification and intensity prediction, ensemble learning significantly improved performance over single-model baselines. in the cross-lingual setting, the translation-based approach yielded notable improvements, underlining the potential of intermediate language translation to bridge linguistic gaps. future work will explore refined data balancing strategies, and the integration of larger advanced models (e.g., llama 3.2, t5) to further enhance performance.