Sentiment Analysis of e-Government Service Using the Naive Bayes Algorithm

Abstract

A digital platform called the Alpukat Population Application is used to handle statistics and information regarding DKI Jakarta's population. Using the Naive Bayes Classifier (NBC) approach, sentiment analysis for applications using satellite placement. The Nave Bayes Classifier technique is utilized for sentiment analysis because of its benefits in modeling and categorizing complicated data. The user reviews and comments gathered from the Google Play Store were the source of the data utilized in this research. Feature extraction using methods like TF-IDF, sentiment labeling on data, and the development of Nave Bayes Classifier models for sentiment classification were all part of the research project. It is anticipated that the study's findings would help us better understand how users interact with the Alpukat population app. This sentiment analysis may assist app administrators and developers in identifying the positives and negatives of applications and planning updates and advancements based on user feedback. It is anticipated that the sentiment classification model created using the Naive Bayes Classifier approach would be able to classify user evaluations into positive, negative, or neutral sentiment categories with a high degree of accuracy. The creation of improved alpukat positioning apps and decision-making may both benefit from this emotive analysis.