Sentiment Analysis on COVID-19 Vaccine using Naive Bayes Classifier, Support Vector Machine and K-Nearest Neighbors

Monika Rani; Dian Prawira; Nurul Mutiah

doi:10.24114/cess.v8i1.40158

Authors

Monika Rani Universitas tanjungpura
Dian Prawira Universitas tanjungpura
Nurul Mutiah Universitas Tanjungpura

DOI:

https://doi.org/10.24114/cess.v8i1.40158

Keywords:

Naive Bayes Classifier, Support Vector Machine, K-Nearest Neighbors, Analisis Sentimen Twitter, Vaksin COVID-19.

Abstract

Procurement of the COVID-19 vaccination has led to diverse opinions among Indonesian people on Twitter. Sentiment analysis on Twitter can be carried out to find out public opinion, especially among Twitter users. The data was used in the form of tweets with the topic of the COVID-19 vaccine using the keywords covid 19 vaccine, covid vaccine, AstraZeneca, Sinovac, Moderna, Pfizer, Novavax and Sinopharm. analysis of the performance of the Naive Bayes Classifier, Support Vector Machine and K-Nearest Neighbors algorithms to determine the results of the accuracy level between the algorithms. The highest classification test is using the Support Vector Machine with an accuracy rate of 0.701. The results of the comparison of algorithms tested using tweet data on the topic of the COVID-19 vaccine found that the Support Vector Machine was better than the Naive Bayes Classifier and K-Nearest Neighbors. From the classification test carried out using COVID-19 vaccine tweet data with 2500 data. The amount of data after going through the data processing process is 1052 data. Neutral sentiment results in as many as 645 positive sentiments as many as 250 and negative sentiments as many as 157.

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