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

Abstract

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.

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“…Previous research studies have explored specific aspects, such as different preprocessing techniques, diverse text features, or tailored models specific to particular domains or topics Schouten & Frăsincar (2016)-Hamzah, 2021) [31]. These studies have delved into various applications of sentiment analysis, ranging from analyzing sentiments toward COVID-19 vaccines [23], traffic risk management [33], hotel reviews [34], public trust in government policies during the pandemic [35], to sentiments related to the COVID-19 booster vaccine [36]. Moreover, sentiment analysis has been conducted on a wide array of subjects, including sentiments towards airlines [37], academic articles [38], Indonesian general analysis datasets [39], Bali tourism during the pandemic [40], internet service providers [41], work from home policies [42], and technology utilization by local governments [43].…”