FinEAS: Financial Embedding Analysis of Sentiment

TL;DR

FinEAS introduces a novel financial sentiment analysis model based on fine-tuned BERT embeddings, significantly outperforming existing models like FinBERT, LSTM, and vanilla BERT in financial text classification tasks.

Contribution

The paper presents a new supervised fine-tuning approach for financial sentiment analysis using BERT sentence embeddings, establishing best practices in this domain.

Findings

FinEAS outperforms FinBERT, LSTM, and vanilla BERT in accuracy.

Supervised fine-tuning of BERT embeddings improves financial sentiment classification.

The approach enhances pattern recognition in financial texts for market analysis.

Abstract

We introduce a new language representation model in finance called Financial Embedding Analysis of Sentiment (FinEAS). In financial markets, news and investor sentiment are significant drivers of security prices. Thus, leveraging the capabilities of modern NLP approaches for financial sentiment analysis is a crucial component in identifying patterns and trends that are useful for market participants and regulators. In recent years, methods that use transfer learning from large Transformer-based language models like BERT, have achieved state-of-the-art results in text classification tasks, including sentiment analysis using labelled datasets. Researchers have quickly adopted these approaches to financial texts, but best practices in this domain are not well-established. In this work, we propose a new model for financial sentiment analysis based on supervised fine-tuned sentence embeddings from a standard BERT model. We demonstrate our approach achieves significant improvements in comparison to vanilla BERT, LSTM, and FinBERT, a financial domain specific BERT.