用基于Transformer的语言模型衡量新闻情绪
Measuring Sentiment News with Transformer-Based Language Models
AI总结：
研究财经新闻情绪衡量，提出用基于Transformer语言模型构建指数框架，经实验将其结果与基准指标对比，并通过人类标注验证，发现该模型能产生更贴近人类评估的情绪指标，与人类判断一致性更强。
AI中文摘要：
从财经新闻中衡量情绪是经济和金融领域的核心任务，但现有大多数指标依赖基于词典的方法，只能部分捕捉语境、否定和语义结构。本文提出用基于Transformer的语言模型构建每日新闻情绪指数的框架，并评估其是否比基于词典的方法更能代表情绪。利用143,755篇财经新闻文章，用FinBERT在句子层面分类情绪，并通过替代归一化方案汇总为文章层面和每日情绪指标。将结果指数与基准指标比较，通过444名参与者对588篇新闻文章的标注验证，发现基于Transformer的指标与人类判断的一致性更强，能更好区分不同情绪文章。结果表明基于Transformer的语言模型能产生更贴近人类评估的情绪指标。
英文摘要：
Measuring sentiment from financial news is a central task in economics and finance, yet most existing indicators rely on dictionary-based approaches that infer sentiment from word counts and only partially capture context, negation, and semantic structure. This paper proposes a framework for constructing daily news mood indices using transformer-based language models and evaluates whether they better represent sentiment than dictionary-based alternatives. Using 143,755 financial news articles from Factiva, we classify sentiment at the sentence level with FinBERT and aggregate these predictions into article-level and daily sentiment measures through alternative normalization schemes. We compare the resulting indices with benchmark measures based on Shapiro et al., 2022 and Barbaglia et al., 2025. A central contribution is the validation of alternative sentiment measures against human judgments. We conducted an incentivized annotation exercise in which 444 participants evaluated a validation subsample of 588 financial news articles. Consensus ratings from independent human evaluations serve as an external benchmark for assessing the quality of automated sentiment measures. Across correlation, regression, and classification exercises, transformer-based measures show stronger agreement with human judgments than vocabulary-based alternatives and perform substantially better in distinguishing positive, neutral, and negative articles. Overall, the results suggest that incorporating contextual information through transformer-based language models produces sentiment measures that more closely reflect human assessments of financial news.
