2026-10-12T10:59:31Z https://www.scidb.cn/oai

10.57760/sciencedb.36305 2026-01-30T16:08:53Z

2026-01-30 The dataset for "A Multilingual Short Text Classification Method Based on In-Context Learning" doi:10.57760/sciencedb.36305 en In this paper, AfriSenti-SemEval is adopted as the experimental dataset. AfriSenti-SemEval is a dataset for the multilingual short text sentiment classification task covering 12 African languages, which includes African languages from different language families with sample sizes ranging from 1,261 to 22,152 for each language. It features cross-language-family diversity, resource imbalance and short text sparsity, and thus demonstrates strong representativeness in the research on multilingual short text sentiment classification. Multilingual; In-Context Learning; Short Text Classification Lin Nankai Ruan Bowen Wang Lianxi RESTRICTED dataset Science Data Bank