用语义图增强微博情感分析，让机器读懂投资者情绪背后的原因。
Semantically Enriching Investor Micro-blogs for Opinion-Aware Emotion Analysis: A Practical Approach
- 构建基于1万条StockTwits评论的语义观点图，细化情绪标签
- 引入图神经网络后，各类情绪分类性能均提升
- 适合金融情绪分析、投资者行为研究者使用
尽管情感分析是金融自然语言处理的核心任务，但捕捉情感背后的‘原因’仍具挑战。现有研究虽尝试结合情绪与情感分析，却无法提供情绪指向目标的细粒度信息。本文通过在StockEmotions数据集上构建语义结构化观点图，为每条语句添加颗粒化的语义深度。利用声明式大模型流水线，从10,000条StockTwits评论中提取观点图。同时，研究引入观点语义对图神经网络（GNN）基线分类器的影响。分析表明，加入观点语义后，在不同情绪维度上的分类性能均有提升。
原文摘要 · Abstract (English)
While sentiment analysis is the staple of financial NLP, capturing the nuances of 'why' behind that sentiment remains a challenge. There have been attempts to address this by analysing investor emotions alongside sentiment; however, this does not provide the additional granularity required to understand the target of the emotion/sentiment. We address this by augmenting the StockEmotions dataset with semantically structured opinion graphs, which provide granular semantic depth to the existing sentiment and emotion labels. Using a declarative LLM pipeline, we augment the StockEmotions dataset with opinion graphs for each sentence, derived from 10,000 comments collected from StockTwits. In addition, we study the effect of introducing opinion semantics on baseline classifiers using Graph Neural Networks (GNNs). Our analysis demonstrates that incorporating opinion semantics improves classification performance across different emotional spectrums
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