Public attitudes analysis to Covid-19 vaccination based on natural language processing

Research Article
Open access

Public attitudes analysis to Covid-19 vaccination based on natural language processing

Yibo Sun 1*
  • 1 University of Nottingham Ningbo China    
  • *corresponding author Scyys9@nottingham.edu.cn
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230911
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

The COVID-19 epidemic has spread globally since 2020, seriously affecting the order of social and economic development and endangering the lives of the people. With the continuous efforts of medical research institutions, the Covid-19 vaccine has been gradually launched, bringing hope for the prevention and treatment of the new coronavirus. However, many people still have doubts about the safety of these rapidly developed vaccines. In order to better promote vaccines to the rest of the public, it is important to determine their attitudes toward providing appropriate vaccines. Thanks to the rapid development of social networks and natural language processing technologies, collecting and analyzing attitude data from social media has proven to be an alternative solution. In this paper, we search for some of the most popular methods to test their efficiency and correctness in analyzing public attitudes toward vaccines. Extensive experimental results have verified the effectiveness of our work, which can provide new insights into automated surveys of attitudes toward Covid-19 vaccines.

Keywords:

natural language processing.

Sun,Y. (2023). Public attitudes analysis to Covid-19 vaccination based on natural language processing. Applied and Computational Engineering,6,1448-1455.
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References

[1]. D. Bajic, V. Dajic, and B. Milovanovic, “Entropy analysis of COVID-19 cardiovascular signals” Entropy, vol. 23, no. 1, p. 87, 2021.

[2]. Prakash M Nadkarni, Lucila Ohno-Machado, Wendy W Chapman, Natural language processing: an introduction, Journal of the American Medical Informatics Association, Volume 18, Issue 5, September 2011, Pages 544–551

[3]. Breiman L. Bagging predictors[J]. 1996, Machine learning, 24(2): 123-140.

[4]. Kingsford C, Salzberg SL. What are decision trees? Nat Biotechnol. 2008 Sep;26(9):1011-3. doi: 10.1038/nbt0908-1011. PMID: 18779814; PMCID: PMC2701298.

[5]. Quinlan J R. Learning decision tree classifiers[J]. ACM Computing Surveys (CSUR), 1996, 28(1): 71-72.

[6]. Song Y Y, Ying L U. Decision tree methods: applications for classification and prediction[J]. Shanghai archives of psychiatry, 2015, 27(2): 130.

[7]. Kass GV. Anexploratory technique for investigating large quantities of categorical data. Appl Stat. 1980;29: 119–127.

[8]. Breiman L. Random forests[J]. Machine learning, 2001, 45(1): 5-32.

[9]. Quinlan RJ. C4.5: Programs for Machine Learning. San Mateo California: Morgan Kaufmann Publishers, Inc.; 1993.

[10]. Loh W, Shih Y. Split selection methods for classification trees. Statistica Sinica. 1997;7: 815–840.

[11]. Bühlmann P, Yu B. Analyzing bagging[J]. The annals of Statistics, 2002, 30(4): 927-961.

[12]. Biau G, Scornet E. A random forest guided tour[J]. Test, 2016, 25(2): 197-227.

[13]. Belgiu M, Drăguţ L. Random forest in remote sensing: A review of applications and future directions[J]. ISPRS journal of photogrammetry and remote sensing, 2016, 114: 24-31.

[14]. Pranckevičius T, Marcinkevičius V. Application of logistic regression with part-of-the-speech tagging for multi-class text classification[C]//2016 IEEE 4th workshop on advances in information, electronic and electrical engineering (AIEEE). IEEE, 2016: 1-5.


Cite this article

Sun,Y. (2023). Public attitudes analysis to Covid-19 vaccination based on natural language processing. Applied and Computational Engineering,6,1448-1455.

Data availability

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

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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)

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References

[1]. D. Bajic, V. Dajic, and B. Milovanovic, “Entropy analysis of COVID-19 cardiovascular signals” Entropy, vol. 23, no. 1, p. 87, 2021.

[2]. Prakash M Nadkarni, Lucila Ohno-Machado, Wendy W Chapman, Natural language processing: an introduction, Journal of the American Medical Informatics Association, Volume 18, Issue 5, September 2011, Pages 544–551

[3]. Breiman L. Bagging predictors[J]. 1996, Machine learning, 24(2): 123-140.

[4]. Kingsford C, Salzberg SL. What are decision trees? Nat Biotechnol. 2008 Sep;26(9):1011-3. doi: 10.1038/nbt0908-1011. PMID: 18779814; PMCID: PMC2701298.

[5]. Quinlan J R. Learning decision tree classifiers[J]. ACM Computing Surveys (CSUR), 1996, 28(1): 71-72.

[6]. Song Y Y, Ying L U. Decision tree methods: applications for classification and prediction[J]. Shanghai archives of psychiatry, 2015, 27(2): 130.

[7]. Kass GV. Anexploratory technique for investigating large quantities of categorical data. Appl Stat. 1980;29: 119–127.

[8]. Breiman L. Random forests[J]. Machine learning, 2001, 45(1): 5-32.

[9]. Quinlan RJ. C4.5: Programs for Machine Learning. San Mateo California: Morgan Kaufmann Publishers, Inc.; 1993.

[10]. Loh W, Shih Y. Split selection methods for classification trees. Statistica Sinica. 1997;7: 815–840.

[11]. Bühlmann P, Yu B. Analyzing bagging[J]. The annals of Statistics, 2002, 30(4): 927-961.

[12]. Biau G, Scornet E. A random forest guided tour[J]. Test, 2016, 25(2): 197-227.

[13]. Belgiu M, Drăguţ L. Random forest in remote sensing: A review of applications and future directions[J]. ISPRS journal of photogrammetry and remote sensing, 2016, 114: 24-31.

[14]. Pranckevičius T, Marcinkevičius V. Application of logistic regression with part-of-the-speech tagging for multi-class text classification[C]//2016 IEEE 4th workshop on advances in information, electronic and electrical engineering (AIEEE). IEEE, 2016: 1-5.