Sentiment Analysis about Anti-LGBT Campaign using the Naïve Bayes Classifier

Abstract

Social media is growing so that the news that is discussed is also very fast to be known by everyone. The news or topic that is being discussed on social media is the anti-LGBT campaign. The conversation about the anti-LGBT campaign is expressed in the form of opinions that contain positive and negative feelings. The opinion is conveyed through Twitter. Twitter is a microblogging social media site that allows users to create short messages and share them easily and quickly. Opinions on Twitter are used to see whether the opinion rejects or supports the anti-LGBT campaign. The use of sentiment analysis helps to see the opinion supports or rejects the anti-LGBT campaign. The algorithm used to perform sentiment analysis is the Naïve Bayes Classifier. The purpose of this study is to determine the sentiment analysis of anti-LGBT campaign tweets on Twitter. This study using Phython as the tools. The dataset used is 3103 tweets with 80% training data and 20% test data. The sentiment analysis results obtained in this study show that Twitter users in Indonesia have more positive opinions. The use of the Naïve Bayes Classifier algorithm produces an accuracy of 68,75%, precision of 99,6%, and recall of 92,8%.