# Multi-Modal Opinion Integration for Financial Sentiment Analysis using Cross-Modal Attention

arXiv: https://arxiv.org/abs/2512.03464 · HTML: https://mlsanity.com/paper/2512.03464/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [SEntFiN 1.0: Entity-Aware Sentiment Analysis for Financial News](https://mlsanity.com/paper/2305.12257/) (2023)
- [FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture](https://mlsanity.com/paper/2508.20212/) (2025)
- [StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series](https://mlsanity.com/paper/2301.09279/) (2023)
- [Knowledge-Guided Dynamic Modality Attention Fusion Framework for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2410.04491/) (2024)
- [Machine Learning based Enterprise Financial Audit Framework and High Risk Identification](https://mlsanity.com/paper/2507.06266/) (2025)

## Abstract (arXiv)

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.