Sentiment Analysis of User Reviews of E-commerce Applications: Case Study on the Shoppe Platform

Abstract

The development of e-commerce in Indonesia is driven by the large population and extensive geography. One of the leading e-commerce applications in Indonesia is Shopee. User reviews of this application reflect public sentiment towards Shopee. The Naive Bayes method was used to classify reviews, with an accuracy rate of 90.76%. Using TF-IDF helps calculate the weight of words in reviews. Performance evaluation shows that the model has high accuracy in scenario 1 with a 60:40 split between training data and test data. The use of information technology has changed the business paradigm, including in e-commerce such as Shopee. The importance of understanding consumer sentiment can be seen from social media platforms, where sentiment analysis using Naive Bayes and Topic Modeling shows negative sentiment at the 11:11 Shopee Flashsale event. Suggestions for Shopee include improving event strategies such as Flashsale by inviting artists and improving customer service. Responsiveness to technical application problems is also emphasized to increase customer satisfaction. The increase in e-commerce transactions in Indonesia shows rapid growth, with many platforms emerging. Sentiment analysis such as that done with K-Nearest Neighbor (K-NN) on Google Play Store reviews provides insight into the experience of e-commerce users in Indonesia, with an accuracy rate of up to 82%. This research provides significant implications for e-commerce companies in better understanding and meeting customer expectations.