BERT for sentiment analysis in the era of epidemic

Research Article
Open access

BERT for sentiment analysis in the era of epidemic

Yanshu Li 1*
  • 1 Soochow University    
  • *corresponding author 2009404142@stu.suda.edu.cn
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230611
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

With the continuous progress of Internet technology, the network platform has gradually entered everyone's life, providing a platform for ordinary people to express their ideas. Since the occurrence of COVID-19, monitoring and analyzing public opinion on the Internet platform has become more practical. Through timely monitoring and analysis, it is of great practical significance for the relevant departments to analyze and control sentiment information and stabilize and guide public sentiment. Therefore, it is essential and of practical significance to select a suitable model for classifying and analyzing public opinion on the Internet platform. This paper reviews the development of word vector technology from the perspective of technology development and then lead to the more advanced Bidirectional Encoder Representations from Transformers (BERT) model with great significance. On this basis, this paper fine-tunes the pre-trained Bert model. It applies the transfer learning strategy to analyzing the public sentiment of the occurrence of COVID-19 during the recent epidemic in Shanghai based on Sina Weibo data. In addition, tests are conducted to compare the model with the previous models. The experimental results show that the Bert model has significant advantages over the traditional model in character vector encoding and feature extraction.

Keywords:

BERT, sentiment analysis, transformer, epidemic.

Li,Y. (2023). BERT for sentiment analysis in the era of epidemic. Applied and Computational Engineering,6,1217-1226.
Export citation

References

[1]. Zhu Huayu,Sun Zhengxing,Zhang Fuyan. An Automatic Chinese Text Classification System Based on Vector Space Model [J]. Computer Engineering, (02):15-17+63.

[2]. Wang Shumei,Dai Baocun,Wu Huizhong,Wang Fei. Text classification dictionary generation [J], Journal of Nanjing University of Science and Technology (Natural Science Edition),2002(05):517-521.

[3]. Zhang Huanjiong, Li Yujian,Zhong Yixin. A New Method for Calculating Text Similarity, Law [J]. Computer Science,2002(07):92-93.

[4]. KIM Y. Convolutional neural networks for sentence classification[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. New York: ACM,2014:1746-1751.2014.9.3.

[5]. KALCHBRENNER N, GREFENSTETTE E, BLUNSOM P.A convolutional neural network for modelling sentences[C]/770-778.2015.12.10.

[6]. Sun Mingmin.Chinese Text Classification Based on GRU Attention [J]. Modern Information Technology,2019,3(03):10-12.

[7]. Liu Yue, Zhai Donghai, Ren Qingning News text classification based on attention CNLSTM network model [J/OL] Computer Engineering: 1-7 [2019-05-21] https://do i.org/10.19678/j.issn.1000-3428.0051312.2019.4.3.

[8]. Liu Xinhui,Chen Wenshi, Zhou Ai,Chen Fei,Qu Wen,Lu Mingyu. Research on multi label text classification based on joint model [J/OL]. Computer Engineering and Application:1-10,[2019-07-18].http://kns.cnki.net/kcms/detail/11.2127.TP.20190624.1809.020.html.2019.6.25.

[9]. Bo Tao, Li Xiaojun, Chen Su, Wang Yuting, Qi Guoliang Rapid seismic intensity assessment method based on social media data [J] Earthquake Engineering and Engineering Vibration, 2018, 38 (5): 206-215

[10]. Zhao, Y. and Fan, B. (2018) Exploring Open Government Data Capacity of Government Agency: Based on the Resource-Based Theory. Gov-ernment Information Quarterly, 35, 1-12.

[11]. Mergel, I., Kleibrink, A. and Sörvik, J. (2018) Open Data Outcomes: US Cities between Product and Process Innovation. Government Infor-mation Quarterly, 35, 622-632.

[12]. Li Zongmin, Zhang Qi, Du Xinyu A study on the effect of rumor dispelling based on the interaction of rumor dispelling microblogs and the emotional tendency of popular comments -- Take the rumor dispelling microblogs related to the Xinguan epidemic as an example [J] Information magazine, 2020, 39 (11): 90-95+110

[13]. Gao, J., Zheng, P., Jia, Y., Chen, H., Mao, Y., Chen, S., Dai, J., et al. (2020) Mental Health Problems and Social Media Exposure during COVID-19 Out-break. PLOS ONE, 15, e0231924.

[14]. Devlin J ,Chang M W ,Lee K ,et al. BERT :Pre-tr aining of Deep Bidirectional Transformers for Language Und erstanding[J].2018.

[15]. Tomas Mikolov ,Kai Chen ,Greg Corrado ,and Jeff rey Dean. Efficient Estimation of Word Representations in V ector Space.In Proceedings of Workshop at ICLR ,2013.

[16]. Peters M E,Neumann M,Iyyer M,et al. Deep contextualized word representations[J]. 2018.

[17]. Vaswani A ,Shazeer N ,Parmar N ,et al.Attention I s All You Need [J]. 2017.

[18]. Yang Yan, Xu Bing, Yang Muyun, Zhao Jingjing. An emotion classification method based on the joint depth learning model [J]. Journal of Shandong University (Science Edition), 2017, 52 (09): 19-25

[19]. J. Devlin, M.-W. Chang, K. Lee and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language Understanding", Proc. NAACL-HLT, pp. 4171-4186, 2019.

Cite this article

Li,Y. (2023). BERT for sentiment analysis in the era of epidemic. Applied and Computational Engineering,6,1217-1226.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Zhu Huayu,Sun Zhengxing,Zhang Fuyan. An Automatic Chinese Text Classification System Based on Vector Space Model [J]. Computer Engineering, (02):15-17+63.

[2]. Wang Shumei,Dai Baocun,Wu Huizhong,Wang Fei. Text classification dictionary generation [J], Journal of Nanjing University of Science and Technology (Natural Science Edition),2002(05):517-521.

[3]. Zhang Huanjiong, Li Yujian,Zhong Yixin. A New Method for Calculating Text Similarity, Law [J]. Computer Science,2002(07):92-93.

[4]. KIM Y. Convolutional neural networks for sentence classification[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. New York: ACM,2014:1746-1751.2014.9.3.

[5]. KALCHBRENNER N, GREFENSTETTE E, BLUNSOM P.A convolutional neural network for modelling sentences[C]/770-778.2015.12.10.

[6]. Sun Mingmin.Chinese Text Classification Based on GRU Attention [J]. Modern Information Technology,2019,3(03):10-12.

[7]. Liu Yue, Zhai Donghai, Ren Qingning News text classification based on attention CNLSTM network model [J/OL] Computer Engineering: 1-7 [2019-05-21] https://do i.org/10.19678/j.issn.1000-3428.0051312.2019.4.3.

[8]. Liu Xinhui,Chen Wenshi, Zhou Ai,Chen Fei,Qu Wen,Lu Mingyu. Research on multi label text classification based on joint model [J/OL]. Computer Engineering and Application:1-10,[2019-07-18].http://kns.cnki.net/kcms/detail/11.2127.TP.20190624.1809.020.html.2019.6.25.

[9]. Bo Tao, Li Xiaojun, Chen Su, Wang Yuting, Qi Guoliang Rapid seismic intensity assessment method based on social media data [J] Earthquake Engineering and Engineering Vibration, 2018, 38 (5): 206-215

[10]. Zhao, Y. and Fan, B. (2018) Exploring Open Government Data Capacity of Government Agency: Based on the Resource-Based Theory. Gov-ernment Information Quarterly, 35, 1-12.

[11]. Mergel, I., Kleibrink, A. and Sörvik, J. (2018) Open Data Outcomes: US Cities between Product and Process Innovation. Government Infor-mation Quarterly, 35, 622-632.

[12]. Li Zongmin, Zhang Qi, Du Xinyu A study on the effect of rumor dispelling based on the interaction of rumor dispelling microblogs and the emotional tendency of popular comments -- Take the rumor dispelling microblogs related to the Xinguan epidemic as an example [J] Information magazine, 2020, 39 (11): 90-95+110

[13]. Gao, J., Zheng, P., Jia, Y., Chen, H., Mao, Y., Chen, S., Dai, J., et al. (2020) Mental Health Problems and Social Media Exposure during COVID-19 Out-break. PLOS ONE, 15, e0231924.

[14]. Devlin J ,Chang M W ,Lee K ,et al. BERT :Pre-tr aining of Deep Bidirectional Transformers for Language Und erstanding[J].2018.

[15]. Tomas Mikolov ,Kai Chen ,Greg Corrado ,and Jeff rey Dean. Efficient Estimation of Word Representations in V ector Space.In Proceedings of Workshop at ICLR ,2013.

[16]. Peters M E,Neumann M,Iyyer M,et al. Deep contextualized word representations[J]. 2018.

[17]. Vaswani A ,Shazeer N ,Parmar N ,et al.Attention I s All You Need [J]. 2017.

[18]. Yang Yan, Xu Bing, Yang Muyun, Zhao Jingjing. An emotion classification method based on the joint depth learning model [J]. Journal of Shandong University (Science Edition), 2017, 52 (09): 19-25

[19]. J. Devlin, M.-W. Chang, K. Lee and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language Understanding", Proc. NAACL-HLT, pp. 4171-4186, 2019.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.