Semantically Enriching Investor Micro-blogs for Opinion-Aware Emotion Analysis: A Practical Approach

2026-05-04

AI Summary

This study addresses the limitations of existing financial sentiment analysis methods, which often fail to identify the semantic targets of expressed emotions and their underlying causes, resulting in insufficient granularity and interpretability. To overcome this, the work introduces opinion graphs into financial Weibo data for the first time, leveraging a declarative large language model (LLM) pipeline to jointly annotate emotions, sentiment polarities, and their associated semantic targets, thereby constructing structured opinion representations. Building upon this framework, a graph neural network (GNN) is employed for emotion classification. Experimental results on the StockEmotions dataset demonstrate that incorporating opinion semantics significantly enhances the performance of various emotion classifiers, validating the effectiveness and novelty of the proposed approach in advancing fine-grained and interpretable financial emotion understanding.

Abstract

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

Problem

• sentiment analysis
• emotion analysis
• opinion target
• semantic granularity
• financial NLP

Innovation

• opinion graphs
• semantic enrichment
• emotion analysis
• graph neural networks
• declarative LLM pipeline