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Published in: Neural Computing and Applications 10/2021

17-10-2020 | S.I. : Higher Level Artificial Neural Network Based Intelligent Systems

Applying BERT to analyze investor sentiment in stock market

Authors: Menggang Li, Wenrui Li, Fang Wang, Xiaojun Jia, Guangwei Rui

Published in: Neural Computing and Applications | Issue 10/2021

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Abstract

This paper is an analysis of investor sentiment in the stock market based on the bidirectional encoder representations from transformers (BERT) model. First, we extracted the sentiment value from online information published by stock investor, using the Bert model. Second, these sentiment values were weighted by attention for computing the investor sentiment indicator. Finally, the relationship between investor sentiment and stock yield was analyzed through a two-step cross-sectional regression validation model. The experiments found that investor sentiment in online reviews had a significant impact on stock yield. The experiments show that the Bert model used in this paper can achieve an accuracy of 97.35% for the analysis of investor sentiment, which is better than both LSTM and SVM methods.

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Metadata
Title
Applying BERT to analyze investor sentiment in stock market
Authors
Menggang Li
Wenrui Li
Fang Wang
Xiaojun Jia
Guangwei Rui
Publication date
17-10-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 10/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05411-7

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