YNWA _ PZ at S em E val-2025 Task 11: Multilingual Multi-Label Emotion Classification (2025)

Abstract: This paper explores multilingual emotion classification across binary classification, intensity estimation, and cross-lingual detection tasks. To address linguistic variability and limited annotated data, we evaluate various deep learning approaches, including transformer-based embeddings and traditional classifiers. After extensive experimentation, language-specific embedding models were selected as the final approach, given their superior ability to capture linguistic and cultural nuances. Experiments on high- and low-resource languages demonstrate that this method significantly improves performance, achieving competitive macro-average F1 scores. Notably, in languages such as Tigrinya and Kinyarwanda for cross-lingual detection task, our approach achieved a second-place ranking, driven by the incorporation of advanced preprocessing techniques. Despite these advances, challenges remain due to limited annotated data in underrepresented languages and the complexity of nuanced emotional expressions. The study highlights the need for robust, language-aware emotion recognition systems and emphasizes future directions, including expanding multilingual datasets and refining models.

## introduction the analysis and processing of emotions from textual data have become crucial in understanding human communication across different languages and cultures. this study focuses on the detection and classification of emotions across diverse linguistic contexts, spanning regions from south america to east asia. our objective is to categorize emotions into key dimensions, namely sadness, anger, fear, disgust, joy, and surprise, while considering crosslingual variations and linguistic complexities. to address these challenges, we structure our study into three distinct tracks: (1) track a in-volves binary emotion classification, determining whether a given text expresses a particular emotion; understanding emotions based on textual data plays a pivotal role in various applications, including social media analysis, behavioral research, and the study of emotions' influence on social interactions. our work contributes to the development of robust emotion recognition systems, enabling better comprehension of multilingual emotional expressions and their implications in computational linguistics. despite the significant advancements in emotion classification, several challenges persist. some languages exhibit highly complex grammatical structures, making it difficult to train effective models. additionally, the classification of emotions in lowresource languages is hindered by data scarcity and syntactic intricacies. furthermore, certain machine learning models demonstrate suboptimal performance when applied to multilingual emotion classification, necessitating the development of novel techniques to enhance model adaptability and generalization. to address these limitations, we present a comprehensive analysis of state-of-the-art methodologies and evaluate their effectiveness across multiple languages. our findings highlight the critical role of innovative preprocessing techniques, domain adaptation strategies, and transfer learning in improving multilingual emotion classification. all code implementations, including the models and experimental setups employed in this study, are publicly available on github: 1 . this repository pro-vides full documentation of our methodologies, experimental results, and final model architectures. ## related work multi-label emotion detection has emerged as a significant task in nlp 2 , particularly for low-resource languages. the task is structured in two main output formats: (1) a binary format, which indicates whether an emotion is present in the text, and (2) an intensity scale ranging from 0 to 3, which represents the strength of the emotion in the text. given that this task follows a text classification paradigm, various models have been explored to identify the most effective architectures. a considerable amount of research has focused on evaluating different structures to determine the optimal approach. in @xcite , a combination of lstm 3 networks and cnns 4 was explored, where various model configurations were compared based on their f1-score performances. these insights were leveraged to identify suitable model structures for developing a custom model tailored to the specific requirements of this task. for low-resource languages, text preprocessing plays a crucial role in improving model performance. the work presented in @xcite highlighted the effectiveness of multiple preprocessing algorithms specifically designed for african languages. the study demonstrated that well-structured preprocessing pipelines lead to better text representations, ultimately improving classification accuracy. moreover, datasets specifically curated for emotion detection in underrepresented languages have been explored. the datasets presented in @xcite and @xcite serve as essential resources for training models and evaluating performance in real-world settings. these datasets enable the training of robust models capable of handling linguistic diversity. to enhance model performance, modifications to existing architectures have been proposed. based on the insights from @xcite , additional layers were incorporated into custom models to improve the representation of low-resource languages. this ensures that the models can capture intricate linguistic patterns that might otherwise be overlooked. ## system overview in this section, we present a comprehensive overview of our system for multi-label text classification, which integrates various deep learning architectures and machine learning classifiers. the system follows a pipeline that includes text preprocessing, feature extraction using neural network models, and classification through different machine learning algorithms. ## experimental setup this section outlines the experimental setup, including data splits, preprocessing, hyperparameter tuning, computational resources, and the tools and libraries used, aiming for reproducibility and transparency. all experiments and evaluation protocols in this work are conducted following the guidelines specified in semeval-2025 task 11 @xcite , which establishes the framework for text-based emotion detection. ## results extensive experiments were conducted on multiple models to determine the most effective approach for multi-label emotion detection across various languages. the selected model was trained on datasets corresponding to each language, and its performance was analyzed using the test dataset. the evaluation results are presented in more detailed results and additional analysis can be found in the appendix a. notably, our approach achieved first rank in the oromo, tigrinya, and kinyarwanda languages in track-c of the competition. this strong performance highlights the effectiveness of the use of language-specific model embeddings tailored to the linguistic characteristics of each language. a comparison of our findings with reference studies @xcite highlights the effectiveness of our approach. by leveraging domainspecific model embeddings, our models were able to bridge the gap in emotion classification for lowresource languages. figure 1 presents the pooled confusion matrices for tracks a, b, and c, highlighting the classifi-cation performance and misclassifications across different intensity levels and languages. ## conclusion this study presented a comprehensive examination of multilingual multi-label emotion detection, addressing binary classification, intensity estimation, and cross-lingual detection tasks. our findings indicate that language-specific embedding models, when paired with classifiers such as svm and xgboost, offer a robust approach to capturing the nuanced linguistic and cultural features inherent in diverse textual data. the experimental results, measured in competitive macro-average f1 scores, underscore the potential of these tailored models to bridge performance gaps, particularly in low-resource languages where data scarcity and complex grammatical structures present significant challenges. the significance of this research lies in its demonstration that integrating innovative preprocessing techniques with state-of-the-art embedding models can lead to substantial improvements in emotion recognition performance. this has broad implications for applications in social media analysis, behavioral research, and other domains where understanding nuanced emotional expressions is crucial. nonetheless, current limitations in multilingual emotion analysis include the lack of annotated data for underrepresented languages and challenges in capturing nuanced emotional expressions, both of which hinder model performance. future research should prioritize expanding multilingual datasets, improving preprocessing techniques, and developing new architectures to boost model generalization and adaptability. fine-tuning models for low-resource languages could also enhance emotion detection accuracy, advancing the field and creating more effective, language-aware emotion recognition systems.