Sentiment analysis of tokopedia application review to service product recommender system using neural collaborative filtering for marketplace in Indonesia
Restu Meifitrah, Irfan Darmawan, Oktariani Nurul Pratiwi
Abstract
Abstract Tokopedia is one of the leading e-commerce companies in Indonesia and ranks second in the top 10 e-commerce in Indonesia in 2018 based on Statista data. Large companies like Tokopedia need to find out what users think of the products or services offered. User opinions on the Tokopedia application can be seen in the review column on the Google Play Store, but processing a review is not easy. To overcome this we need a sentiment analysis method or technique. Sentiment analysis is performed using the Naive Bayes algorithm. The results of positive sentiments obtained are used as a reference to maintain service quality and the results of negative sentiments can be used as an evaluation material in improving Tokopedia services and applications.