FinBERT: Financial Sentiment Analysis with Pre-trained Language Models

Dogu Araci

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arXiv · 1908.10063

FinBERT: Financial Sentiment Analysis with Pre-trained Language Models

Dogu Araci

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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.

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Dogu Araci. 2019-08-27. FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. https://arxiv.org/abs/1908.10063

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