Sentiment Analysis Towards the Film Dirty Vote on Twitter Social Media Using the K-Nearest Neighbor Algorithm

Abstract

The appearance of the dirty vote film has received public attention and went viral on social media after being released and watched by millions of people in a short time. The dirty vote film has become a topic of discussion, one of which is on the social media platform Twitter. This research was conducted to determine the views or tendencies of public opinion regarding dirty vote films on Twitter social media using K-Nearest Neighbor which will be classified into positive, neutral and negative sentiment. The sentiment data that was collected in the data crawling process was 4000 pieces of data. Then after preprocessing there were 3978 data. Labeling was carried out using text blob, it was found that the negative sentiment class was 3470 superior to the positive sentiment class of 451 and the neutral sentiment class was 57. The 10-fold cross validation test produced an average accuracy value of 87.5%. Testing was carried out with 80% training data consisting of 3182 data and 20% test data consisting of 796 test data. The results of sentiment analysis show that the K-Nearest Neighbor method can be used for sentiment analysis. The accuracy value obtained was 87%, precision was 87%, recall was 100%, and f1-score was 93%.