A Financial Multimodal Sentiment Analysis Model Based on Federated Learning

DOI

10.2991/978-94-6463-845-5_84How to use a DOI?

Keywords

Financial Sentiment Analysis; Federated Learning; Multimodal Learning; BERT

Abstract

With the rapid development of financial markets, accurate sentiment analysis has become increasingly crucial for market prediction and risk management. However, traditional centralized approaches face challenges in data privacy and cross-institutional collaboration. This paper proposes a novel financial multimodal sentiment analysis model based on federated learning, which integrates both textual and voice data while ensuring data privacy. The model employs a dual-branch parallel processing architecture with an improved FedAvg algorithm for feature fusion and collaborative training. Experiments were conducted on a dataset containing 4,846 paired text-speech samples from financial news and analyst commentaries. Results demonstrate that our model achieves significant performance in sentiment classification, particularly excelling in neutral sentiment recognition with an accuracy of 316 correct predictions. The model shows good convergence and generalization ability while maintaining data privacy. Although challenges remain in polar sentiment classification, this study provides a new paradigm for privacy-preserving multimodal sentiment analysis in the financial domain.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ziwen Zhong
AU  - Biliang Wang
AU  - Ziang Qi
PY  - 2025
DA  - 2025/09/16
TI  - A Financial Multimodal Sentiment Analysis Model Based on Federated Learning
BT  - Proceedings of the 2025 6th International Conference on Management Science and Engineering Management (ICMSEM 2025)
PB  - Atlantis Press
SP  - 845
EP  - 853
SN  - 2667-1271
UR  - https://doi.org/10.2991/978-94-6463-845-5_84
DO  - 10.2991/978-94-6463-845-5_84
ID  - Zhong2025
ER  -