SENTIMENT ANALYSIS OF E-COMMERCE REVIEWS WITH NATURAL LANGUAGE PROCESSING (NLP) - Idhami, Rahmat; Saputra, Andri; Fadly, Taufa; Silaban, Robet (Journal of Computer Science and Research (JoCoSiR), 2024)
E-commerce in Indonesia is growing rapidly, with Shopee as a leading platform. This study uses Natural Language Processing algorithms to analyze customer satisfaction sentiment from reviews on the Google Play Store. The results identify issues related to courier services and provide recommendations for improving service quality, delivery tracking systems, and overall customer satisfaction and loyalty towards Shopee. This chapter describes the research methodology for sentiment analysis of Shopee reviews using Natural Language Processing methods. These stages include data collection, cleaning, pre-processing, labeling, data separation, classification, and negative word analysis. This study aims to identify the dominant negative sentiment in Google Play Store reviews. This study outlines data scraping, cleaning, pre-processing, labeling, and Natural Language Processing classification to identify negative words in Shopee user reviews. This method provides insights into courier service issues and recommendations for couriers frequently highlighted in reviews, with a focus on future service improvements. Based on the study, Natural Language Processing is effective in identifying positive and negative sentiment in Shopee with an accuracy of 86-87%. Negative sentiment was dominant (62.5%), particularly regarding "recommended couriers," with complaints about delays and unprofessionalism. Recommendations included improving courier service quality, delivery tracking systems, customer communication, and courier training and supervision to improve customer satisfaction.