Sentiment analysis to COVID-19 vaccination based on bert and LSTM

Research Article
Open access

Sentiment analysis to COVID-19 vaccination based on bert and LSTM

Shuyu Fang 1*
  • 1 Shanghai Jiao Tong University, Shanghai, Minhang District, 201100, China    
  • *corresponding author 858167250@sjtu.edu.cn
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230935
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 novel coronavirus (COVID-19) was defined as a pandemic in March 2020, which has brought great harm to the overall economy and people's health. Efforts to suppress and end the COVID-19 have led to an unprecedented vaccine rush, stemming from extensive research by experts and authoritative institutions. Previous research has found that significant numbers of people are hesitant to get vaccinated, worrying there may be some hurt for heath. To this end, a timely understanding of people's sentiments about the COVID-19 vaccination is crucial to the popularization of vaccines. Thanks to the popularity of the mobile Internet, people have become accustomed to expressing their opinions by posting comments on the Internet, which provides a quick way to obtain data for analyzing people's sentiment changes on the COVID-19 vaccine. In this paper, to gain an intuitive understanding of people's acceptance of a particular vaccine, we propose a tweet sentiment analysis method based on Bert and LSTM. Using N-grams to evaluate the model results, an accuracy rate of 74.14% can be obtained, which verifies the effectiveness of the proposed method. Extensive experiments show that our method can provide some new insight for the later vaccination policy promotion to a certain extent.

Keywords:

Sentiment Analyses; n-gram; Bert; LSTM

Fang,S. (2023). Sentiment analysis to COVID-19 vaccination based on bert and LSTM. Applied and Computational Engineering,6,944-951.
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References

[1]. A. Soni, B. Amrhein, M. Baucum, E. J. Paek and A. Khojandi, "Using Verb Fluency, Natural Language Processing, and Machine Learning to Detect Alzheimer’s Disease," 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico, 2021, pp. 2282-2285.doi: 10.1109/EMBC46164.2021.9630371

[2]. Bose P, Roy S, Ghosh P. A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research. IEEE Access. 2021 May 20;9:78341-78355. doi: 10.1109/ACCESS.2021.3082108. PMID: 34786315; PMCID: PMC8545210.

[3]. C. Li, G. Zhan and Z. Li, "News Text Classification Based on Improved Bi-LSTM-CNN," 2018 9th International Conference on Information Technology in Medicine and Education (ITME), Hangzhou, China, 2018, pp. 890-893.doi: 10.1109/ITME.2018.00199

[4]. D. Nagalavi and M. Hanumanthappa, "N-gram Word prediction language models to identify the sequence of article blocks in English e-newspapers," 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bengaluru, India, 2016, pp. 307-311. doi: 10.1109/CSITSS.2016.7779376

[5]. K. Khan and S. Yadav, "Sentiment analysis on covid-19 vaccine using Twitter data: A NLP approach," 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC), Bangalore, India, 2021, pp. 01-06.doi: 10.1109/R10-HTC53172.2021.9641515

[6]. M. Sushmitha, K. Suresh and K. Vandana, "To Predict Customer Sentimental behavior by using Enhanced Bi-LSTM Technique," 2022 7th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2022, pp. 969-975.doi: 10.1109/ICCES54183.2022.9835947

[7]. Q. Mi, Y. Gao, J. Keung, Y. Xiao and S. Mensah, "Identifying Textual Features of High-Quality Questions: An Empirical Study on Stack Overflow," 2017 24th Asia-Pacific Software Engineering Conference (APSEC), Nanjing, China, 2017, pp. 636-641.doi: 10.1109/APSEC.2017.77

[8]. S. S. Kumar and T. Shaikh, "Empirical Evaluation of the Performance of Feature Selection Approaches on Random Forest," 2017 International Conference on Computer and Applications (ICCA), Doha, Qatar, 2017, pp. 227-231.doi: 10.1109/COMAPP.2017.8079769

[9]. T. S. N. Ayutthaya and K. Pasupa, "Thai Sentiment Analysis via Bidirectional LSTM-CNN Model with Embedding Vectors and Sentic Features," 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), Pattaya, Thailand, 2018, pp. 1-6.doi: 10.1109/iSAI-NLP.2018.8692836

[10]. X. Ye and S. Manoharan, "Performance Comparison of Automated Essay Graders Based on Various Language Models," 2021 IEEE International Conference on Computing (ICOCO), Kuala Lumpur, Malaysia, 2021, pp. 152-157.doi: 10.1109/ICOCO53166.2021.9673585

[11]. Yasmin F, Najeeb H, Moeed A, Naeem U, Asghar MS, Chughtai NU, Yousaf Z, Seboka BT, Ullah I, Lin CY, Pakpour AH. COVID-19 Vaccine Hesitancy in the United States: A Systematic Review. Front Public Health. 2021 Nov 23;9:770985. doi: 10.3389/fpubh.2021.770985. PMID: 34888288; PMCID: PMC8650625.

Cite this article

Fang,S. (2023). Sentiment analysis to COVID-19 vaccination based on bert and LSTM. Applied and Computational Engineering,6,944-951.

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]. A. Soni, B. Amrhein, M. Baucum, E. J. Paek and A. Khojandi, "Using Verb Fluency, Natural Language Processing, and Machine Learning to Detect Alzheimer’s Disease," 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico, 2021, pp. 2282-2285.doi: 10.1109/EMBC46164.2021.9630371

[2]. Bose P, Roy S, Ghosh P. A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research. IEEE Access. 2021 May 20;9:78341-78355. doi: 10.1109/ACCESS.2021.3082108. PMID: 34786315; PMCID: PMC8545210.

[3]. C. Li, G. Zhan and Z. Li, "News Text Classification Based on Improved Bi-LSTM-CNN," 2018 9th International Conference on Information Technology in Medicine and Education (ITME), Hangzhou, China, 2018, pp. 890-893.doi: 10.1109/ITME.2018.00199

[4]. D. Nagalavi and M. Hanumanthappa, "N-gram Word prediction language models to identify the sequence of article blocks in English e-newspapers," 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bengaluru, India, 2016, pp. 307-311. doi: 10.1109/CSITSS.2016.7779376

[5]. K. Khan and S. Yadav, "Sentiment analysis on covid-19 vaccine using Twitter data: A NLP approach," 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC), Bangalore, India, 2021, pp. 01-06.doi: 10.1109/R10-HTC53172.2021.9641515

[6]. M. Sushmitha, K. Suresh and K. Vandana, "To Predict Customer Sentimental behavior by using Enhanced Bi-LSTM Technique," 2022 7th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2022, pp. 969-975.doi: 10.1109/ICCES54183.2022.9835947

[7]. Q. Mi, Y. Gao, J. Keung, Y. Xiao and S. Mensah, "Identifying Textual Features of High-Quality Questions: An Empirical Study on Stack Overflow," 2017 24th Asia-Pacific Software Engineering Conference (APSEC), Nanjing, China, 2017, pp. 636-641.doi: 10.1109/APSEC.2017.77

[8]. S. S. Kumar and T. Shaikh, "Empirical Evaluation of the Performance of Feature Selection Approaches on Random Forest," 2017 International Conference on Computer and Applications (ICCA), Doha, Qatar, 2017, pp. 227-231.doi: 10.1109/COMAPP.2017.8079769

[9]. T. S. N. Ayutthaya and K. Pasupa, "Thai Sentiment Analysis via Bidirectional LSTM-CNN Model with Embedding Vectors and Sentic Features," 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), Pattaya, Thailand, 2018, pp. 1-6.doi: 10.1109/iSAI-NLP.2018.8692836

[10]. X. Ye and S. Manoharan, "Performance Comparison of Automated Essay Graders Based on Various Language Models," 2021 IEEE International Conference on Computing (ICOCO), Kuala Lumpur, Malaysia, 2021, pp. 152-157.doi: 10.1109/ICOCO53166.2021.9673585

[11]. Yasmin F, Najeeb H, Moeed A, Naeem U, Asghar MS, Chughtai NU, Yousaf Z, Seboka BT, Ullah I, Lin CY, Pakpour AH. COVID-19 Vaccine Hesitancy in the United States: A Systematic Review. Front Public Health. 2021 Nov 23;9:770985. doi: 10.3389/fpubh.2021.770985. PMID: 34888288; PMCID: PMC8650625.

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