Carter, J.; Nasir, W.; Lee, S.; Parker, E. Dynamics Computational Sentiment Analysis in Financial Markets. Preprints2024, 2024040928. https://doi.org/10.20944/preprints202404.0928.v1
APA Style
Carter, J., Nasir, W., Lee, S., & Parker, E. (2024). Dynamics Computational Sentiment Analysis in Financial Markets. Preprints. https://doi.org/10.20944/preprints202404.0928.v1
Chicago/Turabian Style
Carter, J., Sophia Lee and Ethan Parker. 2024 "Dynamics Computational Sentiment Analysis in Financial Markets" Preprints. https://doi.org/10.20944/preprints202404.0928.v1
Abstract
The flux of opinions in newsletters and social media platforms significantly influences market perceptions regarding corporate entities and their financial instruments. This paper introduces an advanced model, the Sentiment Dynamics Analyzer (SDA), which leverages a refined hierarchical architecture of Transformers to effectively discern the sentiment embedded within financial texts, ranging from news headlines to microblogs. We have enhanced a RoBERTa model with specialized sentiment lexicons and an augmented layer of Transformer models tailored for nuanced sentence-level sentiment detection, aiming to assign a sentiment score from -1 to +1. Our evaluations demonstrate that SDA not only surpasses the previous best methods but also exhibits superior performance over robust baseline models. This success underscores the utility of integrating tailored contextual analyses with sector-specific sentiment insights in improving predictive accuracy.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
