BERT-Based Stock Market Sentiment Analysis

Chien Cheng Lee; Zhongjian Gao; Chun Li Tsai

doi:10.1109/ICCE-Taiwan49838.2020.9258102

BERT-Based Stock Market Sentiment Analysis

Chien Cheng Lee, Zhongjian Gao, Chun Li Tsai

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

This paper explores the performance of natural language processing in financial sentiment classification. We collected people's views on U.S. stocks from the Stocktwits website. The messages on this website reflect investors' views on the stock. These messages are classified into positive or negative sentiments using a BERT-based language model. Investor sentiment can be further analyzed to help more investors, businesses or organizations make effective decisions. The experimental results show that the pre-trained BERT model has been fine-tuned on the labeled sentiment dataset, and can recognize the sentiment of investors with an accuracy of more than 87.3%.

Original language	English
Title of host publication	2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728173993
DOIs	https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258102
Publication status	Published - 2020 Sept 28
Event	7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 - Taoyuan, Taiwan Duration: 2020 Sept 28 → 2020 Sept 30

Publication series

Name	2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Conference

Conference	7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
Country/Territory	Taiwan
City	Taoyuan
Period	20-09-28 → 20-09-30

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Artificial Intelligence
Computer Science Applications
Signal Processing
Electrical and Electronic Engineering
Instrumentation

Access to Document

10.1109/ICCE-Taiwan49838.2020.9258102

Cite this

Lee, C. C., Gao, Z., & Tsai, C. L. (2020). BERT-Based Stock Market Sentiment Analysis. In 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 Article 9258102 (2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258102

@inproceedings{2d27ac251ee2451392638a8b3d17507b,

title = "BERT-Based Stock Market Sentiment Analysis",

abstract = "This paper explores the performance of natural language processing in financial sentiment classification. We collected people's views on U.S. stocks from the Stocktwits website. The messages on this website reflect investors' views on the stock. These messages are classified into positive or negative sentiments using a BERT-based language model. Investor sentiment can be further analyzed to help more investors, businesses or organizations make effective decisions. The experimental results show that the pre-trained BERT model has been fine-tuned on the labeled sentiment dataset, and can recognize the sentiment of investors with an accuracy of more than 87.3%.",

author = "Lee, {Chien Cheng} and Zhongjian Gao and Tsai, {Chun Li}",

note = "Funding Information: ACKNOWLEDGMENT This work was supported by the Far Eastern Int'l Bank and Far EasTone Telecommunications in Taiwan. Publisher Copyright: {\textcopyright} 2020 IEEE.; 7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 ; Conference date: 28-09-2020 Through 30-09-2020",

year = "2020",

month = sep,

day = "28",

doi = "10.1109/ICCE-Taiwan49838.2020.9258102",

language = "English",

series = "2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020",

address = "United States",

}

Lee, CC, Gao, Z & Tsai, CL 2020, BERT-Based Stock Market Sentiment Analysis. in 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020., 9258102, 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020, Institute of Electrical and Electronics Engineers Inc., 7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020, Taoyuan, Taiwan, 20-09-28. https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258102

BERT-Based Stock Market Sentiment Analysis. / Lee, Chien Cheng; Gao, Zhongjian; Tsai, Chun Li.
2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9258102 (2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - BERT-Based Stock Market Sentiment Analysis

AU - Lee, Chien Cheng

AU - Gao, Zhongjian

AU - Tsai, Chun Li

PY - 2020/9/28

Y1 - 2020/9/28

N2 - This paper explores the performance of natural language processing in financial sentiment classification. We collected people's views on U.S. stocks from the Stocktwits website. The messages on this website reflect investors' views on the stock. These messages are classified into positive or negative sentiments using a BERT-based language model. Investor sentiment can be further analyzed to help more investors, businesses or organizations make effective decisions. The experimental results show that the pre-trained BERT model has been fine-tuned on the labeled sentiment dataset, and can recognize the sentiment of investors with an accuracy of more than 87.3%.

AB - This paper explores the performance of natural language processing in financial sentiment classification. We collected people's views on U.S. stocks from the Stocktwits website. The messages on this website reflect investors' views on the stock. These messages are classified into positive or negative sentiments using a BERT-based language model. Investor sentiment can be further analyzed to help more investors, businesses or organizations make effective decisions. The experimental results show that the pre-trained BERT model has been fine-tuned on the labeled sentiment dataset, and can recognize the sentiment of investors with an accuracy of more than 87.3%.

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U2 - 10.1109/ICCE-Taiwan49838.2020.9258102

DO - 10.1109/ICCE-Taiwan49838.2020.9258102

M3 - Conference contribution

AN - SCOPUS:85098461241

T3 - 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

BT - 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Y2 - 28 September 2020 through 30 September 2020

ER -

BERT-Based Stock Market Sentiment Analysis

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