Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia
Abstract
:1. Introduction
Related Works
2. Materials and Methods
2.1. Dataset with Sentiment Extracts
2.2. Topic Modelling with LDA
2.3. Feature Selection
2.4. Sentiment Determinant Association Study
3. Results
3.1. Sentiment Polarity Distribution
3.2. Topic Modelling with LDA
3.3. Feature Selection
3.4. Multinomial Logistics Regression Model
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Topic 1 (Type of Booster) | Topic 2 (Effect of Vaccination) | Topic 3 (Vaccination Program) |
---|---|---|
Pfizer | die | Khairykj |
astrazeneca | Fever | Appointment |
Sinovac | Effect | Selangor |
moody | Clinic | |
Stress | appt | |
symptoms | Walk-in | |
tido | mysejahtera | |
Pain | ||
Side |
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Ong, S.-Q.; Pauzi, M.B.M.; Gan, K.H. Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia. Healthcare 2022, 10, 994. https://doi.org/10.3390/healthcare10060994
Ong S-Q, Pauzi MBM, Gan KH. Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia. Healthcare. 2022; 10(6):994. https://doi.org/10.3390/healthcare10060994
Chicago/Turabian StyleOng, Song-Quan, Maisarah Binti Mohamed Pauzi, and Keng Hoon Gan. 2022. "Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia" Healthcare 10, no. 6: 994. https://doi.org/10.3390/healthcare10060994