Sentiment Analysis Against Political Figure’s Billboard During Pandemic Using Naïve Bayes Algorithm

Abstract

In the midst of the Covid-19 Pandemic, many Indonesians have reacted negatively to the placement of political individuals' billboards with very huge sizes on the streets. The early political campaign that was run was thought to be contentious. On social media like Twitter, the majority of people freely share their thoughts. The purpose of this study is to investigate how the general public reacted to the placement of billboards advertising political figures during the epidemic and to categorize those responses. It is envisaged that it would also provide advice for connected parties that may be used when making judgments regarding the policy of constructing billboards for political figures during a pandemic based on the results of data analysis. Twitter users tend to be more expressive because of the character limits, which means they have sentimental or emotional values. Using the Nave Bayes Algorithm, it is possible to do sentiment analysis on the sentiment data by categorizing user comments into positive, negative, and neutral attitudes. Regarding the sentiments expressed on billboards showing political leaders during the pandemic, tweets were sorted into three categories: liked, unfavorable, and neutral. The accuracy rate from Naive Bayes categorization of political personalities during the pandemic on social media Twitter was 83.3% with a precision value of 89%, recall 83%, and f-1 score of 82%.

Comparison of Machine Learning Methods for Classifying User Satisfaction Opinions of the PeduliLindungi Application

“…The data from text preprocessing that is still in the form of words will be converted to numerical form with a word weighting process that aims to get the weight of each word used as a feature. The result of word weighting with TF-IDF is the multiplication of TF and IDF values which will produce the weight of each word using Equation (1). Tf is term frequency, W is TF-IDF weight, and idf is Inverse Document Frequency.…”

“…The social restriction policy has changed people's behavior which requires physical distance in the process of social interaction. The government itself developed an application that is used to minimize the spread of covid-19, namely the PeduliLindungi application [1]. The PeduliLindungi application is a tracking application to prevent the spread of covid-19 [2].…”