FinBERT: Financial Sentiment Analysis with Pre-trained Language Models
This addresses the problem of specialized language and limited labeled data in finance for analysts and researchers, though it is incremental as it adapts an existing model to a new domain.
The paper tackled financial sentiment analysis by introducing FinBERT, a pre-trained language model based on BERT, which improved state-of-the-art results on two datasets with better metrics and outperformed existing methods even with less training data.
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.