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

TL;DR

This paper enhances financial sentiment analysis by adding semantic opinion graphs to investor comments, improving emotion classification accuracy with a novel dataset and GNN-based methods.

Contribution

It introduces semantically structured opinion graphs for investor comments and demonstrates their effectiveness in improving emotion classification performance.

Findings

Opinion semantics improve classifier accuracy

Augmented dataset enhances emotional nuance understanding

GNNs benefit from semantic opinion structures

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