Multi-Modal Opinion Integration for Financial Sentiment Analysis using Cross-Modal Attention
This work addresses the challenge of capturing fine-grained interactions across opinion modalities for financial market forecasting and risk assessment, representing an incremental improvement.
The paper tackled the problem of integrating diverse opinion modalities for financial sentiment analysis by proposing a cross-modal attention framework, achieving an accuracy of 83.5% and outperforming baselines by 21%.
In recent years, financial sentiment analysis of public opinion has become increasingly important for market forecasting and risk assessment. However, existing methods often struggle to effectively integrate diverse opinion modalities and capture fine-grained interactions across them. This paper proposes an end-to-end deep learning framework that integrates two distinct modalities of financial opinions: recency modality (timely opinions) and popularity modality (trending opinions), through a novel cross-modal attention mechanism specifically designed for financial sentiment analysis. While both modalities consist of textual data, they represent fundamentally different information channels: recency-driven market updates versus popularity-driven collective sentiment. Our model first uses BERT (Chinese-wwm-ext) for feature embedding and then employs our proposed Financial Multi-Head Cross-Attention (FMHCA) structure to facilitate information exchange between these distinct opinion modalities. The processed features are optimized through a transformer layer and fused using multimodal factored bilinear pooling for classification into negative, neutral, and positive sentiment. Extensive experiments on a comprehensive dataset covering 837 companies demonstrate that our approach achieves an accuracy of 83.5%, significantly outperforming baselines including BERT+Transformer by 21 percent. These results highlight the potential of our framework to support more accurate financial decision-making and risk management.