Sentiment Analysis for Indonesian Salt Policy uses Naïve Bayes and Information Gain Methods

Abstract

Salt production is one of the concerns of the Indonesian government. Several government policies such as salt imports have had a major impact on local salt farmers. Other factors are due to the increased demand for salt, decreased domestic salt production which is unfavorable due to weather factors, and the price of imported salt is lower than that of local salt. Many people express their opinions regarding the salt import policy, via Twitter social media. Sentiment analysis can be applied to analyze tweets or writings by the public regarding salt import policies and classify the data. This study uses the naïve Bayes classifier algorithm model as a sentiment classification algorithm on Twitter social media tweets. The classification process uses the Naïve Bayes algorithm. The feature extraction and weighting method is the TF-IDF method. Not all of the features resulting from the TF-IDF process are used, so feature selection is carried out using the information gain method. Model testing was carried out 5 times with 500 data, using feature selection and without feature selection. Without feature selection, the highest accuracy result is 84% at K=4, while without feature selection it produces an accuracy of 71% at K=3, so there is an increase of 13%.