Amado at SemEval-2025 Task 11: Multi-label Emotion Detection in Amharic and English Data

AbstractRecently, social media has become a platform for different human emotions. Although most existing works treat the user’s opinions into a single emotion, the reality is that one user can have more than one emotion at a time, representing multiple emotions at the same time. Multi-label emotion detection is a more advanced and realistic approach, as it acknowledges the complexity of human emotions and their overlapping nature. This paper presents multi-label emotion detection in Amharic and English data. The work is part of SemEval2025 shared task 11, where tasks and datasets are offered by task organizers. To accomplish the aim of the given task, we fine-tune transformers base BERT model, passing through all different workflow pipelines. On unseen test data, the model evaluation achieved 0.6300 and 0.7025 an average macro F1-score for Amharic and English, respectively.