Enhanced Sentiment Analysis for Financial Markets Using Transformer-Based Models and Multi-Modal Data Fusion

Authors: Sandeep Yadav

DOI: https://doi.org/10.5281/zenodo.14535733

Short DOI: https://doi.org/g8wjq6

Country: USA

Full-text Research PDF File:   View   |   Download

Abstract: This research explores advanced sentiment analysis for financial markets by leveraging transformer-based models and multi-modal data fusion. Traditional sentiment analysis often fails to capture nuanced market dynamics, especially when integrating diverse data sources such as financial news, social media, and stock trends. Transformer models, such as BERT and FinBERT, offer contextualized understanding, while multi-modal fusion combines textual, visual, and numerical data for comprehensive analysis. The proposed framework integrates these technologies, achieving significant improvements in predicting market sentiment and asset price movements. Experimental results on financial datasets demonstrate enhanced accuracy and robustness compared to conventional methods. This study highlights the transformative potential of deep learning and data fusion in financial analytics, offering actionable insights for traders, analysts, and portfolio managers navigating volatile markets.

Keywords: Sentiment Analysis, Financial Markets, Transformer Models, Multi-Modal Data Fusion, BERT, FinBERT, Deep Learning, Financial Analytics, Market Sentiment Prediction, Textual Data Integration, Social Media Analysis, Financial News Analysis, Asset Price Movement, Contextualized Representations, Volatility Analysis

Paper Id: 231881

Published On: 2024-09-03

Published In: Volume 12, Issue 5, September-October 2024

Share this