FinEAS: Financial Embedding Analysis of Sentiment

Abstract

In this article, the authors introduce a new language representation model for sentiment analysis of financial text called financial embedding analysis of sentiment (FinEAS). The new approach is based on transformer language models that are explicitly developed for sentence-level analysis. By building upon Sentence-BERT, a sentence-level extension of vanilla BERT, the authors argue that the new approach produces sentence embeddings of higher quality that significantly improve sentence/document-level tasks such as financial sentiment analysis. Using a large-scale financial news dataset from RavenPack, they demonstrate that for financial sentiment analysis the new model outperforms several state-of-the-art models such as BERT, a bidirectional LSTM, and FinBERT, a financial-domain-specific BERT. The authors make the model code publicly available.