UIMP -Aaman at S em E val-2025 Task11: Detecting Intensity and Emotion in Social Media and News (2025)

Abstract: This paper presents our participation in SemEval task 11, which consists of emotion recognition in sentences written in multiple languages. We use in-context learning and fine-tuning methods to teach LLMs how to predict labels for Track A, Track B and Track C. The best results depends on track and language predicted.

## introduction in the 21st century, emotions remain a fascinating and complex subject of study. from an evolutionary perspective, they have played a vital role in human survival, shaping decision-making, fostering social bonds, and influencing intelligence by bridging reason and instinct. despite significant scientific progress, emotions remain an enigmatic phenomenon that is difficult to fully understand due to their subjective nature. this complexity makes their analysis particularly challenging. in this study, we focus on emotion recognition in text, specifically in sentences written in multiple languages, in the context of the semeval task 11 @xcite . the languages selected for the analysis are english, german, spanish, and brazilian portuguese. our approach begins by examining the range of emotions present in the text files for each language @xcite . then, we outline the specific tasks involved in each track, outline our analytical methods, and describe the experiments conducted. finally, we present the results from our best-performing experiments. the study is structured around three tasks: track a, track b, and track c. in the first track, we will experiment with different models in order to find the one with best performance, including the llama 3 series of models @xcite and the phi-4 model @xcite , using the huggingface transformers library @xcite . then, for track b and c, we will implement the best model for each of these tracks. we report results using macro f1 metric computed over the dev set. ## track a in track a, the emotions to identify are anger, fear, joy, sadness, surprise, and disgust. however, it is important to note that disgust is not present in english or spanish. in our analysis, we will use two key techniques: in-context learning and fine-tuning. in the first technique (in-context learning), we will provide the model with a prompt made up of training examples and their corresponding labels. the weights of the model are not updated, instead the provided examples are used by the model to learn the general pattern of the problem. on the other hand, finetuning involves taking a pre-trained model, which has learned general language patterns from large datasets, and adapting it to perform well on more specialized tasks using specific, domain-related data. together, these techniques allow the model to leverage prior knowledge while tailoring its capabilities to address the specific needs of our task, enhancing both accuracy and efficiency. ## track c track c involves predicting the perceived emotion labels for a new text instance in a target language, using a labeled training set from one of the languages in track a. the prompt used is identical to the one in track a, and for all languages, 40 examples are provided in the prompt. the results are reported on figure 1 . we have decided to approach this task finding the best combination for each language in test. for instance, to predict english, we will train with german, spanish and portuguese dataset. the results obtained are 0.65 with german training, 0.69 with spanish and 0.89 with portuguese, which is the one that outperforms the other languages. moreover, spanish best prediction is made with portuguese training with a performance of 073. in contrast, for german, spanish is the one with higher f1-score with 0.67. finally, portuguese is the best training predictor for spanish. it is interesting to notice that english and spanish are the languages with highest scores. ## final rankings the final results of the competition are reported on ## conclusions we have presented our participation at the semeval task 11, which consists on emotion recognition in text, specifically in sentences written in multiple languages. in our approach we have implemented in-context learning and fine-tuning methods to for each evaluation language, we report the performance depending on which of the other 3 languages is selected as training set.