P romotion G o at S em E val-2025 Task 11: A Feature-Centric Framework for Cross-Lingual Multi-Emotion Detection in Short Texts (2025)

Abstract: This paper presents our system for SemEval 2025 Task 11: Bridging the Gap in Text-Based Emotion Detection (Track A), which focuses on multi-label emotion detection in short texts. We propose a feature-centric framework that dynamically adapts document representations and learning algorithms to optimize language-specific performance. Our study evaluates three key components: document representation, dimensionality reduction, and model training in 28 languages, highlighting five for detailed analysis. The results show that TF-IDF remains highly effective for low-resource languages, while contextual embeddings like FastText and Contextual String Embeddings (CSEs) exhibit language-specific strengths. Principal Component Analysis (PCA) reduces training time without compromising performance, particularly benefiting FastText and neural models such as Multi-Layer Perceptrons (MLP). Computational efficiency analysis underscores the trade-off between model complexity and processing cost. Our framework provides a scalable solution for multilingual emotion detection, addressing the challenges of linguistic diversity and resource constraints.

## introduction emotion labeling in natural language processing (nlp) is critical for enabling machines to better interpret human emotional expressions, fostering empathetic and context-aware ai systems. traditional single-label emotion detection oversimplifies human affect by assigning a single dominant emotion to text, ignoring the complex spectrum of overlapping emotions often present in real-world scenarios @xcite . in contrast, multi-label emotion detection aligns more closely with authentic human experiences, where texts may simultaneously express multiple emotions (e.g., joy and surprise, or sadness and anger). it provides ecologically valid representations of emotional complexity, better reflecting nuanced psychological states. however, multi-label emotion detection can be challenging, such as models must account for emotion co-occurrence, resolve subtle semantic ambiguities, and avoid overfitting to sparse or imbalanced label distributions @xcite @xcite . traditional approaches to multi-label emotion detection have predominantly relied on featurecentric frameworks that leverage handcrafted linguistic and statistical features. while effective in monolingual settings, such frameworks often required language-specific resources (e.g., lexicons for each target language @xcite ), limiting cross-lingual scalability. these features (e.g., lexicons, syntactic patterns) often fail to model the complex interdependencies and contextual nuances required for multi-label emotion detection, as they struggle to capture dynamic label correlations and contextualized affective semantics @xcite @xcite . in this paper, we conduct a comprehensive study on the development of a feature-centric framework to address the challenges of cross-lingual adaptability and multi-label emotion detection through a three-stage methodological pipeline: (1) feature extraction/document representation, (2) dimensionality reduction and (3) model training. for (1), we unify diverse feature representations ranging from interpretable shallow features (e.g., tf-idf, bagof-words) to contextually rich embeddings (e.g., fasttext, bpe) and transformer-based semantic encodings (e.g., sentence-bert). for (2), we reduce the dimensionality of document representations to prevent model overfitting and accelerate the subsequent step. for (3), we systematically evaluate traditional machine learning classifiers (e.g., svm, rf) and deep learning architectures (e.g., mlp) to optimize label dependency modeling. the modular design allows interchangeable classifier integration, balancing interpretability (via traditional models) and performance (via neural approaches) for diverse multilingual use cases. the source code for this paper is publicly available on github 1 . ## background and system overview the development pipeline within the system comprised three distinct stages: (1) feature representation utilizing a diverse set of techniques such as tf-idf, fasttext and sentence-transformers, (2) dimensionality reduction of feature vectors via principal component analysis (pca), and (3) model training and prediction employing a suite of algorithms such as decision trees (dt) and multi-layer perceptrons (mlp). ## experimental data we use the brighter dataset @xcite provided by semeval 2025 task 11 track a to conduct our experiments., it consists of human-annotated short texts in 28 languages, such as english, german and russian. the training dataset comprises 65,098 multi-label samples, each annotated with emotion labelsanger, fear, joy, sadness, surprise, and disgustrepresenting the emotions most likely experienced by the speaker, as inferred from the text. we used only the provided datasets during the development and evaluation phases, no additional training data was introduced to boost the performance. ## results we perform experiments in 28 languages and evaluate model performance using the f1-macro score. additionally, we record time consumption and generate confusion matrices to further analyze the models' performance.