Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler: Twitter

Abstract

Sentiment analysis is an activity carried out to see the level of public sentiment or public opinion relating to goods or services and even a figure, both political and celebrity figures. In this study, a sentiment analysis application for twitter analysis was conducted on 2019 Republic of Indonesia presidential candidates, using the python programming language. There are several steps taken to conduct this sentiment analysis, which is to collect data using libraries in python, text processing, testing training data, and text classification using the Naive Bayes method. The Naive Bayes method is used to help classify classes or the level of sentiments of society. The results of this study found that the value of the positive sentiment polarity of the Jokowi-Ma'ruf Amin pair was 45.45% and a negative value of 54.55%, while the Prabowo-Sandiaga pair received a positive sentiment score of 44.32% and negative 55.68%. Then the combined data was tested from the training data used for each presidential candidate and get an accuracy of 80.90% ≈ 80.1%. In this study a comparison was carried out using the naive bayes, svm and K-Nearest Neighbor (K-NN) methods which were tested using RapidMiner by producing a naive bayes accuracy value of 75.58%, svm accuracy value of 63.99% and K-NN accuracy value of 73.34%.