FinBERT: Financial Sentiment Analysis with Pre-trained Language Models

TL;DR

FinBERT is a domain-specific language model based on BERT that significantly improves financial sentiment analysis by leveraging pre-training on financial corpora, outperforming existing methods with less labeled data.

Contribution

Introduction of FinBERT, a pre-trained language model tailored for financial NLP tasks, demonstrating superior performance over prior models with limited labeled data.

Findings

FinBERT outperforms state-of-the-art methods on financial sentiment datasets.

FinBERT requires fewer labeled examples for effective training.

Fine-tuning part of FinBERT yields significant accuracy improvements.

Abstract

Financial sentiment analysis is a challenging task due to the specialized language and lack of labeled data in that domain. General-purpose models are not effective enough because of the specialized language used in a financial context. We hypothesize that pre-trained language models can help with this problem because they require fewer labeled examples and they can be further trained on domain-specific corpora. We introduce FinBERT, a language model based on BERT, to tackle NLP tasks in the financial domain. Our results show improvement in every measured metric on current state-of-the-art results for two financial sentiment analysis datasets. We find that even with a smaller training set and fine-tuning only a part of the model, FinBERT outperforms state-of-the-art machine learning methods.