  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.
