NUST Titans at S em E val-2025 Task 11: A fro E mo: Multilingual Emotion Detection with Adaptive A fro- XLM - R (2025)

Abstract: This paper presents AfroEmo, a multilingual, multi label emotion classification system designed for SemEval 2025 Task 11, leveraging the Afro XLMR model. Our approach integrates adaptive pretraining on domain specific corpora followed by fine tuning on low resource languages. Through comprehensive exploratory data analysis, we assess label distribution and model performance across diverse linguistic settings. By incorporating perceived emotions, how emotions are interpreted rather than explicitly stated, we enhance emotion recognition capabilities in underrepresented languages. Experimental results demonstrate that our method achieves competitive performance particularly in Amharic, while addressing key challenges in low resource emotion detection.

## introduction emotion detection refers to the process of identifying and interpreting human emotions by analyzing indicators such as body language, tone of voice, facial expressions, and physiological signals. it has broad applications across multiple domains, such as in mental health for detecting depression and emotional distress @xcite @xcite , enhancing customer service engagement @xcite , improving cross-cultural communication @xcite and in conversational agents to create more emotionally intelligent interactions @xcite . traditionally, emotion detection is performed in a monolingual setting. however, researchers have attempted to shift the paradigm toward multilingual emotion detection. shifting from monolingual to multilingual emotion detection poses challenges, including data scarcity, linguistic diversity, reduced model interpretability across languages @xcite @xcite and the complexities of code-switching @xcite @xcite . for example, detecting sentiment in an urdu-english code-mixed sentence like 'i am feeling so udaas today" ("udaas" meaning "sad" in urdu) requires nuanced interpretation that many models lack. low-resource languages exacerbate these issues @xcite , as existing pre-trained models often focus on resource-rich languages @xcite @xcite , thus, failing to capture emotional subtleties in underrepresented languages @xcite . to address key challenges, semeval-2025 task 11 @xcite introduces a large-scale, low-resource multilingual emotion dataset. this dataset includes 32 languages from seven language families, featuring many underrepresented languages from africa, asia, and latin america. it contains over 100,000 instances, manually annotated by native speakers across six emotion classes: joy, sadness, fear, anger, surprise, and disgust, with emotion intensity on a 4-point likert scale (0 to 3). consequently, in this work, we present the following contributions: ## dataset analysis we perform an exploratory data analysis to gain insights of distribution of emotional indices across languages. a strong class imbalance is evident, with anger and joy dominating most languages, particularly amharic, igbo, and yoruba. in contrast, hausa exhibits a relatively more balanced distribution. sundanese stands out with joy as the most prevalent emotion. the limited representation of surprise, disgust and fear across most languages highlights the challenge of developing robust multilingual emotion classifiers, especially under low-resource and imbalanced conditions. ## proposed model we propose afroemo, a multilingual emotion detection model built upon the afro-xlm-r large architecture @xcite , which comprises 24 transformer layers, each with 16 self-attention heads and a 4096-dimensional feedforward network. as shown in figure 2 , our training process consists of two stages: adaptive pre-training on domain-specific corpora and fine-tuning on a labeled multilingual emotion dataset. the implementation and data are publicly available on github. 1 . hereafter, we call our proposed model as afroemo. ## results we evaluate the performance of afroemo against multilingual baselines using standard metrics given by organizers. ## discussion