BERT-Based Stock Market Sentiment Analysis

Chien Cheng Lee, Zhongjian Gao, Chun Li Tsai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publication2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173993
DOIs
Publication statusPublished - 2020 Sep 28
Event7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 - Taoyuan, Taiwan
Duration: 2020 Sep 282020 Sep 30

Publication series

Name2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Conference

Conference7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
Country/TerritoryTaiwan
CityTaoyuan
Period20-09-2820-09-30

All Science Journal Classification (ASJC) codes

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

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