大规模多语言情感数据集语料库与多维度情感分类基准
Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark
AI总结：
本研究构建了覆盖27种语言、含79个高质量数据集的大规模多语言情感分析语料库，并推出汇总数百组实验的多维度情感分类基准，为多语言情感模型训练与评估提供支撑。
摘要：
尽管多语言语料库构建与模型训练已取得显著进展，大规模部署多语言模型仍面临严峻挑战，这在依赖文化背景的语言任务中尤为突出，多语言情感分析就是典型领域——其中的情感标记可能十分微妙，且深植于文化语境之中。本研究推出了目前规模最大的公开大规模多语言情感模型训练数据集语料库：基于严格的质量标准，从科学文献中报道的350余个数据集中人工筛选出79个，覆盖分属6个语系的27种语言，可通过多种语言学特征与功能特征检索数据集。此外，我们还提出了一个多维度情感分类基准，汇总了在不同基础模型、训练目标、数据集组合与微调策略上开展的数百组实验结果。
英文摘要：
Despite impressive advancements in multilingual corpora collection and model training, developing large-scale deployments of multilingual models still presents a significant challenge. This is particularly true for language tasks that are culture-dependent. One such example is the area of multilingual sentiment analysis, where affective markers can be subtle and deeply ensconced in culture. This work presents the most extensive open massively multilingual corpus of datasets for training sentiment models. The corpus consists of 79 manually selected datasets from over 350 datasets reported in the scientific literature based on strict quality criteria. The corpus covers 27 languages representing 6 language families. Datasets can be queried using several linguistic and functional features. In addition, we present a multi-faceted sentiment classification benchmark summarizing hundreds of experiments conducted on different base models, training objectives, dataset collections, and fine-tuning strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
