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The covid-19 pandemic has made changes in society, including Government policy. The policy changes led to mixing responses from the public, namely netizens. Netizen shares their opinion in social media, including Twitter. Their opinion can represent the public’s trust in the Government. Sentiment analysis analyses others’ opinions and categorises them into positive opinions, negative opinions, or neutral opinions. Sentiment analysis can analyze large numbers of opinions so that public opinion can be analyzed quickly. This paper explains how to analyze public trust using sentiment analysis and to use Naïve Bayes classification method to analyze sentiment. The data research was taken from Twitter in the first quarter of the Covid-19 pandemic, with around 3000 tweets. The tweets were related to Covid-19 and the Government from several countries such as the United States, Australia, Ireland, Switzerland, Italy, Philippines, Sri Lanka, Canada, Netherlands, United Kingdom, Germany, and Lebanon. This study aims to determine the level of public trust in the Government in the first quarter of the Covid-19 pandemic. The research result is expected to be used as a reference for the public policy stakeholders to determine future policies.

Prediction of The Level of Public Trust in Government Policies in the 1st Quarter of The Covid 19 Pandemic using Sentiment Analysis

Prediction of The Level of Public Trust in Government Policies in the 1^st Quarter of The Covid 19 Pandemic using Sentiment Analysis