ANALISIS SENTIMEN TERHADAP VAKSIN COVID-19 DI JEJARING SOSIAL TWITTER MENGGUNAKAN ALGORITMA NAÏVE BAYES - Rizal Al Habsi (2021)
Twitter is a platform that allows people to express their aspirations, opinions, and criticisms directly. Since the outbreak of the corona virus or COVID-19 there have been many public opinions related to COVID-19, one of which is related to the COVID-19 vaccine policy. This study aims to find out how public opinion is formed against the COVID-19 vaccine in Indonesia on Twitter with the hashtags #vaksincovid19 and #vaksincorona. In this study, using the concept of public opinion which is categorized into positive and negative sentiments, the Naïve Bayes algorithm is used for the tweet classification process. The data retrieval process starts on January 13 to January 20, 2021 using data crawling techniques by utilizing the API facilities provided by Twitter. The results of the classification will enter the testing and evaluation phase using a confusion matrix to see the performance of the classification model against public opinion regarding the COVID-19 vaccine. From the results of testing the naïve Bayes classification model, the results obtained are 82.65% accuracy, 98% recall and 66.67% specificity. From the results of data labeling using 488 tweets, 251 positive sentiment tweets were obtained that were pro against the COVID-19 vaccine, and 237 tweets of negative sentiment were against the COVID-19 vaccine.