E3S Web Conf.
Volume 317, 2021The 6th International Conference on Energy, Environment, Epidemiology, and Information System (ICENIS 2021)
|Number of page(s)||8|
|Section||Information System Management and Environment|
|Published online||05 November 2021|
Prediction of The Level of Public Trust in Government Policies in the 1st Quarter of The Covid 19 Pandemic using Sentiment Analysis
1 Department of Information Systems, Institut Teknologi Telkom Purwokerto, Central Java, Indonesia
2 Departement of Information Systems, Institut Teknologi Telkom Purwokerto, Central Java, Indonesia
* Corresponding author: email@example.com.
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.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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