Sentimental Analysis of COVID-19 Vaccine Tweets Using BERT plus NBSVM

被引:0
作者
Umair, Areeba [1 ]
Masciari, Elio [1 ]
Madeo, Giusi [2 ]
Ullah, Muhammad Habib [1 ]
机构
[1] Univ Naples Federico II, I-80125 Naples, Italy
[2] IC Rende Commenda, Arcavacata Di Rende, CS, Italy
来源
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT I | 2023年 / 1752卷
关键词
Sentimental analysis; Vaccine; COVID-19; Vaccine hesitancy;
D O I
10.1007/978-3-031-23618-1_16
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The development of the vaccine for the control of COVID-19 is the need of hour. The immunity against coronavirus highly depends upon the vaccine distribution. Unfortunately, vaccine hesitancy seems to be another big challenge worldwide. Therefore, it is necessary to analysis and figure out the public opinion about COVID-19 vaccines. In this era of social media, people use such platforms and post about their opinion, reviews etc. In this research, we proposed BERT+NBSVM model for the sentimental analysis of COVID-19 vaccines tweets. The polarity of the tweets was found using TextBlob(). The proposed BERT+NBSVM outperformed other models and achieved 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. Thus, these sentimental and spatial analysis helps in world-wide pandemics by identify the people's attitudes towards the vaccines.
引用
收藏
页码:238 / 247
页数:10
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