FinBERT: Financial Sentiment Analysis with Pre-trained Language Models

Autor:	Araci, Dogu
Rok vydání:	2019
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: This thesis is submitted in partial fulfillment for the degree of Master of Science in Information Studies: Data Science, University of Amsterdam. June 25, 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1908.10063 Zobrazit plný text záznamu View this record from Arxiv