xFE-BERT: The Way to the Interpretable Financial Text Analysis

xFE-BERT: The Way to the Interpretable Financial Text Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article xFE-BERT: The Way to the Interpretable Financial Text Analysis Md. Asgor Hossain Reaj, Mushfiqur Rahman Abir, Md Arifur Rahman, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7786713/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract As financial institutions want openness and accountability in their automated systems, the task of understanding model choices has become more crucial in the field of financial text analysis. In this study, xFE-BERT, an enhanced method is introduced that uses Feature Extracted Bidirectional Encoder Representations from Transformers (FE-BERT), an architecture based on linearization in phrase structure, to improve explainability in financial sentence prediction. The model is able to extract contextual information from financial texts that is subtle because xFE-BERT combines the BERT architecture with cutting-edge methods specif- ically designed for the financial industry. xFE-BERT offers comprehensible and interpretable insights into model predictions using a mix of feature extracted fine-tuned pre-trained BERT model and explainability approaches LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlana- tions), and Anchors. Our thorough tests on industry-standard financial datasets show that xFE-BERT provides improved transparency and outperforms existing models in terms of prediction accuracy of 98.86%. This paper paves the way for more interpretable and reliable Artificial Intelligence (AI) applications in finance, ensuring that complex models remain accountable to human scrutiny. sentiment analysis finance BERT xAI interpretability Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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