用词向量分析克罗地亚新闻25年语义变迁，发现疫情后情绪反而更积极。
Characterizing Linguistic Shifts in Croatian News via Diachronic Word Embeddings
- 分时段训练词向量，捕捉克罗地亚新闻中关键词的语义演变
- 检测到新冠疫情、入欧等重大事件引发的显著语言变化
- 发现2020年后词向量反映情绪更积极，与心理健康下降研究相反
衡量词语语义随时间的变化有助于理解文化与视角的演变。虽然以往研究依赖大量时间标注语料，本文使用涵盖过去25年的950万篇克罗地亚新闻文章，通过在五年时间段上训练skip-gram词向量来量化语义变化。分析显示，词向量能有效捕捉与重大议题相关的词汇语义迁移，如新冠疫情、克罗地亚加入欧盟及技术进步。此外，2020年后训练的词向量在情感分析任务中表现出更高积极性，与同期心理健康状况下降的研究结果形成对比。
原文摘要 · Abstract (English)
Measuring how semantics of words change over time improves our understanding of how cultures and perspectives change. Diachronic word embeddings help us quantify this shift, although previous studies leveraged substantial temporally annotated corpora. In this work, we use a corpus of 9.5 million Croatian news articles spanning the past 25 years and quantify semantic change using skip-gram word embeddings trained on five-year periods. Our analysis finds that word embeddings capture linguistic shifts of terms pertaining to major topics in this timespan (COVID-19, Croatia joining the European Union, technological advancements). We also find evidence that embeddings from post-2020 encode increased positivity in sentiment analysis tasks, contrasting studies reporting a decline in mental health over the same period.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。