Analisis Sentimen Distribusi Vaksin COVID-19 di Indonesia Menggunakan Algoritma Naïve Bayes Classifier

Kevin Manurip, Debi Irawan


Since the Indonesian government officially announced the first case of COVID-19, traditional media and social media content related to COVID-19 has increased dramatically. On the one hand, the media talks about prevention, symptom recognition and about prevention, symptom recognition and treatment are massive. Sentiment Analysis or commonly called opinion mining, is a field of study that analyzes opinions, sentiments, evaluations, judgments, attitudes, and emotions towards entities and is implemented on social media content. This becomes interesting and important for certain parties who want to know the good and bad sentiments or opinions given by the Indonesian people towards the distribution of vaccines for the handling of COVID-19. From this research, the level of capability of the system that has been built to find the accuracy between the information requested by the user on the Sinovac vaccine results from a total of 1524 tweets, there are 819 positive tweets, 452 neutral tweets, and 253 negative tweets. The results of the AstraZeneca vaccine classification resulted in 211 tweets with a total of 100 positive sentiments, 80 tweets of neutral sentiment, and 31 tweets of negative sentiment. Sentiment classification results based on scraping data with the keyword Astrazeneca vaccine, resulted in 1266 tweets with a positive sentiment value of 712 tweets, neutral sentiment as many as 344 tweets, and negative sentiment as many as 210 tweets.


Classification; Naïve Bayes Classifier; Sentiment Analysis; TextBlob, Covid-19; Vaccine Distribution

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