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 transformer-based document representations, such as those produced by Sentence-BERT, 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.

翻译：本文介绍了我们为 SemEval 2025 任务 11：弥合基于文本的情感检测差距（赛道 A）所开发的系统，该任务专注于短文本中的多标签情感检测。我们提出了一种以特征为中心的框架，该框架动态调整文档表示和学习算法，以优化特定语言的性能。我们的研究在 28 种语言中评估了三个关键组成部分：文档表示、降维和模型训练，并重点分析了其中五种语言。结果表明，TF-IDF 对于低资源语言仍然非常有效，而诸如 FastText 的上下文嵌入和基于 Transformer 的文档表示（例如由 Sentence-BERT 生成的表示）则展现出语言特定的优势。主成分分析（PCA）在不影响性能的前提下减少了训练时间，尤其使 FastText 和诸如多层感知机（MLP）的神经网络模型受益。计算效率分析强调了模型复杂性与处理成本之间的权衡。我们的框架为多语言情感检测提供了一个可扩展的解决方案，应对了语言多样性和资源限制带来的挑战。