Classification of Twitter User Sentiments Against Government Policies in Overcoming Covid-19 in Indonesia

Abstract

Sentiment classification is a field of study that analyzes a person's opinions, sentiments, judgments, evaluations, attitudes, and emotions regarding a particular topic, service, product, individual, organization, or activity. The topic that is currently being discussed is Covid-19. Covid19 is a disease caused by the corona virus, which was first identified in the city of Wuhan, China. This disease has spread throughout the world, including Indonesia. In this regard, the Indonesian government issued a policy as an effort to break the chain of the spread of the corona virus. However, this encourages the emergence of various kinds of public responses. One of them is Twitter users, there are pros and cons responses from the community in responding to government policies and causing problems, namely the difficulty of knowing positive, neutral or negative responses given by the community. Based on the explanation above, a sentiment analysis was carried out. This analysis was carried out by utilizing data from Twitter with the keywords at home, vaccines for the people of Indonesia, and PSBB, covid, covid19, covid Indonesia, vaccines Jakarta, vaccines, vaccines Restore RI, and vaccines for the sake of protecting the Republic of Indonesia. Where the data will be processed through several stages, namely preprocessing, word weighting and sentiment analysis. The results of the classification of the sentiment classification of the majority of Twitter users are neutral, namely 69.2% of the data classified as neutral sentiment, 30.1% of the data classified as positive sentiment, and 7% of the data having negative sentiment.