The Effect of Feature Weighting on Sentiment Analysis TikTok Application Using The RNN Classification

Abstract

Social media is a medium used by people to express their opinions. In its development, social media has become a necessity in social life. One of the most popular social media applications since 2020 is TikTok. Short videos with an average duration of 60 seconds can entertain the community so that they don't feel isolated. There are 17 million TikTok application reviews in the Google Play store in Indonesia from various user ages. The rapid development of information and technology has led to the pros and cons of this application. Freedom of expression without specific restrictions on content publication negatively impacts the user's mentality. Based on this, sentiment analysis is very important to reveal trends in opinions about applications that are useful for the community in increasing awareness of whether the application is good before use. Proper feature weighting is required to improve the sentiment analysis results' accuracy. More optimal results can be obtained by determining the appropriate weight for different feature weighting. This study compares the TF IDF, TF RF, and Word2Vec feature weighting methods with the RNN classifier on the TikTok app review. The experiment shows that TF RF is superior to TF IDF, with successive feature weighting accuracy with TF RF of 87,6%, TF IDF of 86%, and Word2Vec of 80%. The contribution of this research lies in its exploration of different feature weighting methods to enhance sentiment analysis accuracy and provide valuable insights for decision-making processes.