Sentiment Analysis in Projects to Support Indonesian Government Induction Stove Project: A Systematic Literature Review

Abstract

Since the rise in popularity of text-based social media platforms, millions of individuals have expressed their perspectives and ideas on a variety of intriguing topics. With this information, one can comprehend how the public interprets product preferences, political movements, marketing campaigns, and corporation methods. Frequently, sentiment analysis is used to examine the public's perspective on a variety of topics. Sentiment analysis evaluates attitude information from textual data, often utilizing lexicon-based such as Dictionary-based techniques and machine-learning algorithms for textual categorization such as Support Vector Machines, Neural Networks, Long-short Term Memory, and many more. Recently, there has been intense debate in Indonesia regarding a government initiative to switch from gas to induction stoves. The pros and cons in the community on this subject give stakeholders the opportunity to compile the perspectives and concerns of the Indonesian. From this study, there are potential approaches that can be used to identify public sentiment on the Indonesian government induction stoves project, which are Support Vector Machines, Logistic Regression, Naïve Bayes Classifier, and K-Nearest Neighbor. The sentiment data that has been generated will eventually be utilized to evaluate performance of the project and provide recommendations to stakeholders to improve the quality of the project.