Sentiment Analysis Pedulilindungi Tweet Using Support Vector Machine Method

Abstract

Pedulilindungi application has many benefits but many controversies arise in the community. Various opinions in the form of tweets were expressed by the public, both positive and negative opinions. In this study, the objective is to make a classification model to classify tweets into two types of sentiment, namely positive and negative. The model is made in several stages, namely data retrieval, data filtering, data labeling, data preprocessing, splitting data train and data test, feature selection using Information Gain and Genetic Algorithm, and then classification using the SVM method. The model using two-stage feature selection and SVM method, obtained an accuracy value of 64.08% with 841 features and processing time of 0.033 seconds with 9.6% CPU usage. The model with two-stage feature selection is more efficient and effective than the one-stage feature selection model whose accuracy value is only 60.56% with 1800 features and a processing time of 0.044 seconds with 15.4% CPU usage.