Analisis Sentimen Terhadap Vaksin Covid-19 di Indonesia pada Twitter Menggunakan Metode Lexicon Based - Putri Amira Sumitro; Rasiban; Dadang Iskandar Mulyana; Wahyu Saputro (E-Jurnal Universitas Samudra, 2021)
Increasing the number of cases of Covid-19 in Indonesia, the Government of Indonesia has made various efforts in handling Covid-19, one of which is the policy of Covid-19 vaccine for all indonesian people. With this policy, many people express their opinions through Twitter. The purpose of this sentiment analysis research is to find out public opinion about the policy of vaccine covid-19 on Twitter whether the opinion falls into the class of sentiment that is categorized into 5 namely very positive sentiment, positive sentiment, negative sentiment, somewhat negative or neutral sentiment and know the accuracy of the percentage of each class of polarity. Lexicon based research method using vader sentiment library. Vader sentiment, which is a lexicon-based method of analysis based on rule-based sentiment analysis. Based on the accuracy of the percentage obtained by positive sentiment as much as 20.25%, somewhat positive as much as 23.9%, negative as much as 1.88%, somewhat negative as much as 9.6% and neutral as much as 44.36%. It can be concluded that public opinion on the Covid-19 vaccine on Twitter using lexicon-based methods by utilizing vader sentiment library is mostly neutral.