Sentiment Analysis on COVID-19 Vaccination in Bangladesh
4th International Conference on Sustainable Technologies for Industry 4.0, STI 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2323924
ABSTRACT
The COVID-19 pandemic has caused a shocking loss of life on a worldwide scale and influenced every sector of Bangladesh very badly. The simplest method for preventing infectious diseases is vaccination. Bangladeshi netizens discuss their opinions, feelings, and experiences associated with the COVID-19 vaccination program on social media platforms. The purpose of this research is to conduct a sentiment analysis of the vaccination campaign, and for this purpose, the reactions of Bangladeshi netizens on social media to the vaccination program were collected. The dataset was manually labelled into two categories positive and negative. Then process the dataset using Natural Language Processing (NLP). The processed data is then classified using various machine learning algorithms using N-gram as a feature extraction method. The recall, precision, f1-score, and accuracy of various algorithms are all measured. The experiment results show that 61% of the reviews indicate the positive aspects of the vaccination program, while 39% are negative. For unigram, bigram, and trigram, the very best accuracy was achieved by Logistic Regression (LR) at 80.70%, 79.45%, and 78.65%. © 2022 IEEE.
Bangla NLP; COVID 19; Machine Learning; Sentiment Analysis; Vaccination; Learning algorithms; Logistic regression; Social networking (online); Vaccines; Bangla natural language processing; Bangladesh; Language processing; Loss of life; Machine-learning; Natural languages; Netizen; SIMPLE method; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
/
Qualitative research
Topics:
Vaccines
Language:
English
Journal:
4th International Conference on Sustainable Technologies for Industry 4.0, STI 2022
Year:
2022
Document Type:
Article
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