Sentiment Analysis of Floods on Twitter Social Media Using the Naive Bayes Classifier Method with the N-Gram Feature

Abstract

Indonesia is 6th largest population affected by floods in the world, which is 640.000 people every year. Indonesia areas that often experience floods due to high-intensity rainfall and tropical climate. Recently, there was a flood in South Kalimantan on January 14, 2021. From this incident, few netizen expressed their opinions about the natural flood disaster through Twitter social media. In this study, the author will classify netizen views regarding the natural flood disaster so that the netizen is aware of the incident and they can prevent flood causes. We will divide the tweet into relevant and irrelevant categories to categorize the incident using the Naïve Bayes Classifier. This research implements N-gram features to consider the most efficient method for determining a classification. We use Naïve Bayes because it assumes all variables are unique and provides weight to the text data using N-Gram. The importance of text data could be used to create a Naïve Bayes Classification model to calculate the probability. The naïve Bayes method can be implemented in classifying natural flood disasters. The tweet within the result using bigram will give higher accuracy than unigram or trigram. According this study the goverment can have plan for future mitigation action.