Comparison of the Accuracy of Sentiment Analysis on the Twitter of the DKI Jakarta Provincial Government during the COVID-19 Vaccine Time

Abstract

Currently, the Government is intensively utilizing social media, one of which is Twitter as a place of interaction with the community. The results of these interactions can be used as feedback to determine whether public opinion on public policies is positive or negative. Tweets from users can be a supporting parameter for the government in evaluating future policies and decision making by applying the sentiment analysis method. This study aims to determine positive or negative sentiments on user tweets against the official twitter account of the DKI Jakarta Provincial Government during the COVID19 vaccine period. The data obtained are 1658 lines from March 30 to April 5, 2021 with queries on tweets containing words or mentioning the username @dkijakarta, which will be grouped by sentiment class, namely negative and positive using the TF-IDF Vectorizer for word weighting and classification using several methods, namely, nave Bayes with accuracy values. 82.50% with class recall on positive sentiment 88% and negative 77% and in class precision showing positive at 79.28% and negative at 86.52% in the rapid miner application then k-NN with an accuracy value of 81.50% with class recall on positive sentiment 85% and negative 78% and class precision shows positive at 79.44% and negative at 83.87% in the rapid miner application. And the accuracy value of the best method in this training data classification comparison is nave Bayes, the results the end of testing the sample dataset using the nave Bayes method with 84.80% accuracy with class recall at 85.01% positive sentiment and 84.59% negative sentiment and at c lass precision shows positive at 85.21% and negative at 84.38% in rapid mining applications.