Amado at S em E val-2025 Task 11: Multi-label Emotion Detection in A mharic and E nglish Data (2025)

Abstract: Amado at SemEval-2025 Task 11: Multi-label Emotion Detection inAmharic and English DataGirma Yohannis Bade, Olga Kolesnikova, José Luis OropezaGrigori Sidorov, Mesay Gemeda Yigezua(Centro de Investigaciones en Computación(CIC),Instituto Politécnico Nacional(IPN), Miguel Othon de Mendizabal,Ciudad de México, 07320, México.)

## introduction in the current digital era, people can openly share their thoughts, sentiments, disagreements, opinions, and attitudes on websites, microblogs, and social media platforms. for a number of reasons, including decision-making, product analysis, customer feedback analysis, political promotions, marketing research, and social media monitoring, there is more interest in obtaining these user's attitudes and emotions all around @xcite . however, all those feedbacks come from different feelings and thoughts, thus becoming complicated and needs those emotions to be organized in certain sort of correlations @xcite . emotion detection is a critical area of research in natural language processing (nlp) that focuses on identifying and understanding the emotional states expressed in text. emotions play a vital role in human communication, influencing how messages are interpreted and how individuals respond to one another. automatically detecting emotions from text has significant applications in various fields, such as mental health analysis, customer feedback evaluation, social media monitoring, and humancomputer interaction. by understanding emotions, systems can provide more personalized and empathetic responses, enhancing user experience and decision-making processes. in emotion detection, a single emotion refers to the identification of one dominant emotion in a given text. for example, a sentence like "i am so happy today!" would be classified as expressing the emotion "joy." this approach assumes that each text conveys only one primary emotion, which simplifies the task but may not fully capture the complexity of human expression. in reality, people often express multiple emotions simultaneously, as emotions are rarely isolated and can coexist in nuanced ways. this is where multi-label emotion detection comes into play. unlike single-emotion classification, multi-label emotion detection recognizes that a single text can express multiple emotions at once. for instance, a sentence like "i feel excited but also a bit nervous about the upcoming event" could be labeled with both "joy" and "fear." multilabel emotion detection is a more advanced and realistic approach, as it acknowledges the complexity of human emotions and their overlapping nature. it allows for a richer and more accurate representation of the emotional content in text, making it particularly valuable for applications requiring a deeper understanding of human sentiment. this paper presents our contribution to semeval 2025 shared task 11 @xcite , where the tasks and gold standard dataset were provided by the organizers. the shared task consists of three tracks; however, our team participated exclusively in track_a, which focuses on detect-ing multiple emotions from textual inputs across various languages @xcite . through this participation, we aim to advance the development of more inclusive and accurate emotion detection systems for amharic and english data in detailed contexts. ## related works recent researches on emotion detection have leveraged various approaches. @xcite proposed a deep learning architecture based on a bidirectional gated recurrent unit with a multichannel convolutional neural network layer to tackle the issue of identifying numerous emotions from patient reports, collecting a sizable patient viewpoints from a website, and identified several emotions from these evaluations, achieving average accuracy of 95.82%. @xcite addresses the multiple emotions detection in online social networks from a user-level view, finding emotion labels correlations, social correlations, and temporal correlations from an annotated twitter data set. they use a factor graph-based emotion recognition model to detect these multiple emotions, and finally it outperformed the baselines. for multi-label emotion classification, @xcite examined the application of lstms as well as the refinement of transformer networks using transfer learning in conjunction with a singleattention network and a multiple-attention network. according to the experimental results, their innovative transfer learning models that used pre-trained transformers with and without multiple attention mechanisms were able to achieve an accuracy of 62.4%, surpassing the state-of-the-art on roberta-ma (roberta-multi-attention). @xcite offer a unique pyramid attention network (pan) based approach for microblog emotion identification, emphasizing the approach's benefit in capturing many emotions present in a single text by evaluating words from several angles, with an accuracy of 58.9%. to help with context-based multi-label multitask emotion detection, @xcite suggests a new deep learning architecture that emphasizes three key modules: body features extraction, scene features extraction, and fusion-decision. furthermore, a new loss function called multi-label focal loss (mfl) was chosen to handle imbalanced data after comparing three continuous and three categorical loss functions to highlight the signifi-cance of synergy between loss functions in multitask learning. it outperformed the state of the art on the less frequent labels and produced better results than any other combination. likewise, @xcite show the methods that have three parts: the general representation module, the emotion representation module, and the adversarial classifier. after incorporating the link between various emotions using emotion descriptors, the model employs adversarial training to avoid over-injecting emotion-relevant data into the shared layer, achieving a macro-average f1 scores of 50.21%, 41.33%, and 40.24% on the chinese, english, and indonesian datasets, respectively. belay et al. (2025) presented ethioemo, a multi-label emotion classification dataset for four ethiopian languages-amharic (amh), afan oromo (orm), somali (som), and tigrinya (tir)-alongside the english dataset acquired from semeval 2018 task 1 to assess encoder-only, encoder-decoder, and decoder-only language models using zero and few-shot approaches of llms to fine-tune smaller language models, concluding that accurate multi-label emotion classification is still insufficient, even for high-resource languages like english, and that there is a significant performance gap between high-resource and low-resource languages. muhammad et al. (2025a) introduce brighter-a set of datasets with multilabeled emotion annotations in 28 languages. with instances from a variety of domains annotated by fluent speakers, brighter primarily covers low-resource languages from africa, asia, eastern europe, and latin america. it also describes the processes involved in data collection and annotation, as well as the difficulties in creating these datasets. it reports various experimental results for intensity-level emotion recognition and monolingual and crosslingual multi-label emotion identification, and it concludes that the results of the investigation, both with and without the use of llms, showed significant variability in performance across languages and text domains. and surprise except 'disgust' label belongs to only amharic data. the task aims to categorize the given input text into either of these class labels, the input text may fall in more than one labels. while ## methodology in this section a comprehensive set of methods and methodologies is described. our approach optimizes and fine-tunes transformer-based models of bert-base to improve emotion detection performance. figure 1 shows the methodology workflow. ## error analysis and limitation the investigation demonstrates that the task of classifying emotions is difficult and even complex for human competence. this demonstrates the importance of this task in assessing the current models and noting that, even for high-resource languages like english, multi-label emotion categorization requires further research. the inability to pinpoint the writers' precise emotions-which requires context-and the overlap between some emotion classes, like anger and disgust (for instance, anger and disgust may appear together, and joy and surprise may also exhibit similarities) are some of the challenges. despite many constraints, our model has demonstrated a comparable performance in detecting multilabel emotions. ## conclusion and future work this study investigated the use of a transformerbased model for multilabel emotion recognition in both amharic and english. according to the study, the transformer-based bert model produced the top f1-scores for both the english and amharic datasets, indicating an effective outcome. the study's findings support the notion that transformer models are highly capable of classifying text in multilabel emotions. to improve the accuracy rate, the next stage should focus on utilizing large datasets, improved fine-tuning techniques, and contextual factors. s-47854 of conacyt, mexico, grants 20241816, 20241819, and 20240951 of the secretaría de investigación y posgrado of the instituto politécnico nacional, mexico. the authors thank the cona-cyt for the computing resources brought to them through the plataforma de aprendizaje profundo para tecnologías del lenguaje of the laboratorio de supercómputo of the inaoe, mexico and acknowledge the support of microsoft through the microsoft latin america phd award.