Analyzing Sentiment of i.Saku E-Wallet Play Store Reviews Using Support Vector Machine

Abstract

The digital era has transformed lifestyles, with people increasingly relying on electronic devices as tools for everyday life. Technology simplifies various activities by streamlining data and information processing. The growing demand for data has driven the development of new technologies to process information quickly. Technological advancements have improved transportation, access to information, education, and the convenience of online transactions, particularly through digital wallets or e-wallets. Digital financial services, including e-wallets, facilitate easy transactions using e-wallet funds. The primary reason for their use is the convenience of electronic wallets, which eliminate the need for cash and simplify transactions for both buyers and sellers. The case of i.Saku demonstrates its popularity, with over 5 million downloads on the Google Play Store since 2017. Google's digital platform, Google Play Store, includes user reviews as a valid source of information. Nearly 50% of internet users rely on recommendations from other users before using a product. Google Play Store reviews influence the decisions of potential users, but managing them manually is not easy. Sentiment analysis, or opinion mining, is the study of opinions, behaviors, and feelings of individuals towards an entity and is crucial for understanding user reviews. In the context of i.Saku, this research focuses on sentiment analysis of Google Play Store reviews using support vector machine techniques. The study outlines the stages from preprocessing to sentiment analysis, highlighting the complexity and benefits of technology-based sentiment analysis.