V erba N ex AI at S em E val-2025 Task 11 Track A: A R o BERT a-Based Approach for the Classification of Emotions in Text (2025)

Abstract: Emotion detection in text has become a highly relevant research area due to the growing interest in understanding emotional states from human interaction in the digital world. This study presents an approach for emotion detection in text using a RoBERTa-based model, optimized for multi-label classification of the emotions joy, sadness, fear, anger, and surprise in the context of the SemEval 2025 - Task 11: Bridging the Gap in Text-Based Emotion Detection competition. Advanced preprocessing strategies were incorporated, including the augmentation of the training dataset through automatic translation to improve the representativeness of less frequent emotions. Additionally, a loss function adjustment mechanism was implemented to mitigate class imbalance, enabling the model to enhance its detection capability for underrepresented categories. The experimental results reflect competitive performance, with a macro F1 of 0.6577 on the development set and 0.6266 on the test set. In the competition, the model ranked 47th, demonstrating solid performance against the challenge posed.

## introduction emotion recognition in text has gained significant relevance with the rise of social networks, facing the challenge of identifying explicit and implicit emotions. the basic emotions most studied include joy, sadness, fear, anger, disgust, and surprise. advances in nlp have driven the development of more accurate models, such as deep neural networks and pre-trained models like roberta, improving emotion classification in various fields such as business, psychology, and security @xcite . this study addresses emotion detection in semeval 2025 subtask a -task 11: bridging the gap in text-based emotion detection. the central problem motivating this research lies in the complexity of accurately detecting and classifying emotions in texts through multi-label prediction. the main objective of this task is to identify perceived emotions in five main categories (joy, sadness, fear, anger, and surprise) using a multi-label classification approach @xcite . although the competition addresses multiple languages, this study focuses exclusively on english. for this purpose, we employed the pre-trained roberta model, leveraging its ability to capture advanced linguistic representations and improve emotion detection in complex texts. furthermore, we implemented strategies to address the class imbalance, optimizing the model's overall performance and maximizing its classification accuracy. the results obtained were competitive, as our system reached the 70th position in the competition, achieving an acceptable performance. however, we identified challenges related to the representation of less frequent emotions, affecting the accuracy of minority classes. these findings highlight the importance of continuing to explore class balancing techniques and model tuning to improve emotion detection in texts. the repository is available via the following link: (available after reviewing.) foot_0 ## background the dataset used in this task comes from brighter, a collection of multilingual datasets for text-based emotion recognition that spans 28 languages. for its construction, authors collected texts from various sources, including social media (reddit, twitter, youtube, weibo), speeches, personal narratives, literature, and news. depending on the language, the data were obtained from specific platforms or manually generated by native speakers. subsequently, the texts were curated and annotated by experts and collaborators through crowdsourcing platforms such as amazon mechanical turk and toloka and specialized tools like la-belstudio and potato. the annotation process included assigning multiple emotion labels and the intensity of each emotion on a scale from 0 to 3. the author selected only english for this study, with data primarily extracted from reddit. the dataset distribution is as follows: 2,768 instances for training, 116 samples for development, and 2,767 records for testing. the structured each instance into three primary columns: id, text, and emotional labels corresponding to joy, sadness, fear, anger, and surprise. ## system overview the based the proposed system for emotion detection in texts on an architecture that combines the pre-trained roberta model with additional classification and data balancing mechanisms @xcite . we designed the architecture to handle the inherent complexity of emotion classification, allowing for precise differentiation of emotions such as anger, fear, joy, sadness, and surprise. figure 2 illustrates the system's structure, showing the processing flow from data input to prediction generation. to delve into the internal functioning of the system, we described some details of its components below. ## experimental setup we designed the experimental configuration to ensure a robust and generalizable emotion detection model. the dataset was initially divided into training and testing sets, assigning 80% for training and the remaining 20% for testing. the original dataset consisted of 2,768 instances in english, which we considered insufficient to capture the complexity of emotional phenomena fully. to address this limitation, we incorporated 7,597 instances from german and brazilian portuguese datasets. we translated the instances into english using the deepl pro api (version v2), which based the learning on the linguee service, an extensive database of phrases and text fragments translated by humans @xcite . thanks to this data source, the api achieves high accuracy in translations. this strategy enriched the training set and improved the model's generalization ability across various structures and linguistic contexts. moreover, the dataset expansion through high-quality translation not only increased data diversity but also contributed to a significant improvement in the model's performance, particularly in detecting underrepresented emotions. with the dataset duly expanded, a rigorous preprocessing procedure was applied to ensure the uniformity and quality of the textual information. ## results in the results obtained in the different evaluation phases, as shown in tables 3 , 4 , and 5 in the development and test sets, during training, the model showed a progressive improvement, reaching a micro precision of 0.9958 and a micro f1-score of 0.9917 in the last epoch. however, the average over all epochs (precision of 0.8694 and loss of the error analysis reveals that the model struggles to identify surprise, sadness, and fear accurately. false positives in surprise suggest that the model confuses emphatic expressions, even when they convey fear or disbelief. similarly, false negatives in sadness indicate that the model does not adequately capture the emotional subtext when we use metaphors or figurative expressions without explicit terms like "sad" or "devastated." additionally, the model has issues with ambiguous phrases where the emotion depends on the context, leading to omissions in detecting fear and joy. ## conclusion automatic emotion classification in text analysis has applications in social networks, digital platforms, the business sector, and education. given its relevance, it is essential to improve the accuracy and robustness of the models, ensuring their adaptability to multiple languages and domains. the proposed system has demonstrated effectiveness in detecting complex emotional nuances, achieving solid results in various categories. however, challenges persist in the identification of minority emotions such as anger and surprise. to address them, it was proposed to implement advanced class balancing strategies and automatic hyperparameter tuning techniques. future work will consider search methods to optimize hyperparameters in several pretrained models and will explore hybrid approaches that integrate complementary architectures.