FinEAS: Financial Embedding Analysis of Sentiment
This work addresses the problem of analyzing financial sentiment for market participants and regulators, but it is incremental as it builds on existing BERT methods.
The authors tackled financial sentiment analysis by proposing FinEAS, a model based on supervised fine-tuned sentence embeddings from BERT, which achieved significant improvements over vanilla BERT, LSTM, and FinBERT.
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.