BERT for stock market sentiment analysis

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Standard

BERT for stock market sentiment analysis. / Sousa, Matheus Gomes; Sakiyama, Kenzo; Rodrigues, Lucas De Souza et al.
IEEE 31st International Conference on Tools with Artificial Intelligence: ICTAI 2019 : proceedings : 4-6 November 2019, Portland, Oregon. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc., 2019. p. 1589-1593 ( International Conference on Tools with Artificial Intelligence, ICTAI).

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Harvard

Sousa, MG, Sakiyama, K, Rodrigues, LDS, Moraes, PH, Fernandes, E & Matsubara, ET 2019, BERT for stock market sentiment analysis. in IEEE 31st International Conference on Tools with Artificial Intelligence: ICTAI 2019 : proceedings : 4-6 November 2019, Portland, Oregon. International Conference on Tools with Artificial Intelligence, ICTAI, IEEE - Institute of Electrical and Electronics Engineers Inc., Piscataway, pp. 1589-1593, IEEE International Conference on Tools with Artificial Intelligence, Portland, United States, 04.11.19. https://doi.org/10.1109/ICTAI.2019.00231

APA

Sousa, M. G., Sakiyama, K., Rodrigues, L. D. S., Moraes, P. H., Fernandes, E., & Matsubara, E. T. (2019). BERT for stock market sentiment analysis. In IEEE 31st International Conference on Tools with Artificial Intelligence: ICTAI 2019 : proceedings : 4-6 November 2019, Portland, Oregon (pp. 1589-1593). ( International Conference on Tools with Artificial Intelligence, ICTAI). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICTAI.2019.00231

Vancouver

Sousa MG, Sakiyama K, Rodrigues LDS, Moraes PH, Fernandes E, Matsubara ET. BERT for stock market sentiment analysis. In IEEE 31st International Conference on Tools with Artificial Intelligence: ICTAI 2019 : proceedings : 4-6 November 2019, Portland, Oregon. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc. 2019. p. 1589-1593. ( International Conference on Tools with Artificial Intelligence, ICTAI). doi: 10.1109/ICTAI.2019.00231

Bibtex

@inbook{ed504f80ec034a1a9df1a4a24498fd70,
title = "BERT for stock market sentiment analysis",
abstract = "When breaking news occurs, stock quotes can change abruptly in a matter of seconds. The human analysis of breaking news can take several minutes, and investors in the financial markets need to make quick decisions. Such challenging scenarios require faster ways to support investors. In this work, we propose the use of bidirectional encoder representations from transformers BERT to perform sentiment analysis of news articles and provide relevant information for decision making in the stock market. This model is pre-trained on a large amount of general-domain documents by means of a self-learning task. To fine-tune this powerful model on sentiment analysis for the stock market, we manually labeled stock news articles as positive, neutral or negative. This dataset is freely available and amounts to 582 documents from several financial news sources. We fine-tune a BERT model on this dataset and achieve 72.5% of F-score. Then, we perform some experiments highlighting how the output of the obtained model can provide valuable information to predict the subsequent movements of the Dow Jones Industrial (DJI) Index.",
keywords = "BERT, NLP, Sentiment analysis, Stock market, Informatics, Business informatics",
author = "Sousa, {Matheus Gomes} and Kenzo Sakiyama and Rodrigues, {Lucas De Souza} and Moraes, {Pedro Henrique} and Eraldo Fernandes and Matsubara, {Edson Takashi}",
year = "2019",
month = nov,
doi = "10.1109/ICTAI.2019.00231",
language = "English",
isbn = "978-1-7281-3799-5",
series = " International Conference on Tools with Artificial Intelligence, ICTAI",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
pages = "1589--1593",
booktitle = "IEEE 31st International Conference on Tools with Artificial Intelligence",
address = "United States",
note = "IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2019 ; Conference date: 04-11-2019 Through 06-11-2019",
url = "https://ictai.computer.org/",

}

RIS

TY - CHAP

T1 - BERT for stock market sentiment analysis

AU - Sousa, Matheus Gomes

AU - Sakiyama, Kenzo

AU - Rodrigues, Lucas De Souza

AU - Moraes, Pedro Henrique

AU - Fernandes, Eraldo

AU - Matsubara, Edson Takashi

N1 - Conference code: 13

PY - 2019/11

Y1 - 2019/11

N2 - When breaking news occurs, stock quotes can change abruptly in a matter of seconds. The human analysis of breaking news can take several minutes, and investors in the financial markets need to make quick decisions. Such challenging scenarios require faster ways to support investors. In this work, we propose the use of bidirectional encoder representations from transformers BERT to perform sentiment analysis of news articles and provide relevant information for decision making in the stock market. This model is pre-trained on a large amount of general-domain documents by means of a self-learning task. To fine-tune this powerful model on sentiment analysis for the stock market, we manually labeled stock news articles as positive, neutral or negative. This dataset is freely available and amounts to 582 documents from several financial news sources. We fine-tune a BERT model on this dataset and achieve 72.5% of F-score. Then, we perform some experiments highlighting how the output of the obtained model can provide valuable information to predict the subsequent movements of the Dow Jones Industrial (DJI) Index.

AB - When breaking news occurs, stock quotes can change abruptly in a matter of seconds. The human analysis of breaking news can take several minutes, and investors in the financial markets need to make quick decisions. Such challenging scenarios require faster ways to support investors. In this work, we propose the use of bidirectional encoder representations from transformers BERT to perform sentiment analysis of news articles and provide relevant information for decision making in the stock market. This model is pre-trained on a large amount of general-domain documents by means of a self-learning task. To fine-tune this powerful model on sentiment analysis for the stock market, we manually labeled stock news articles as positive, neutral or negative. This dataset is freely available and amounts to 582 documents from several financial news sources. We fine-tune a BERT model on this dataset and achieve 72.5% of F-score. Then, we perform some experiments highlighting how the output of the obtained model can provide valuable information to predict the subsequent movements of the Dow Jones Industrial (DJI) Index.

KW - BERT

KW - NLP

KW - Sentiment analysis

KW - Stock market

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85081094676&partnerID=8YFLogxK

U2 - 10.1109/ICTAI.2019.00231

DO - 10.1109/ICTAI.2019.00231

M3 - Article in conference proceedings

AN - SCOPUS:85081094676

SN - 978-1-7281-3799-5

T3 - International Conference on Tools with Artificial Intelligence, ICTAI

SP - 1589

EP - 1593

BT - IEEE 31st International Conference on Tools with Artificial Intelligence

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

CY - Piscataway

T2 - IEEE International Conference on Tools with Artificial Intelligence

Y2 - 4 November 2019 through 6 November 2019

ER -

DOI