Measuring e-Commerce service quality from online customer review using sentiment analysis

Abstract

The biggest e-Commerce challenge to understand their market is to chart their level of service quality according to customer perception. The opportunities to collect user perception through online user review is considered faster methodology than conducting direct sampling methodology. To understand the service quality level, sentiment analysis methodology is used to classify the reviews into positive and negative sentiment for five dimensions of electronic service quality (e-Servqual). As case study in this research, we use Tokopedia, one of the biggest e-Commerce service in Indonesia. We obtain the online review comments about Tokopedia service quality during several month observations. The Naïve Bayes classification methodology is applied for the reason of its high-level accuracy and support large data processing. The result revealed that personalization and reliability dimension required more attention because have high negative sentiment. Meanwhile, trust and web design dimension have high positive sentiments that means it has very good services. The responsiveness dimension have balance sentiment positive and negative.