Sentiment Analysis of the Top 5 E-commerce Platforms in Indonesia using Text Mining and Natural Language Processing (NLP)

Abstract

This research attempts to depict a sentiment comparison of the top 5 E-commerce platforms in Indonesia by gathering the emotional tone behind sentence contents related to customer sentiments, customer experiences, and the brand reputation of E-commerce. Data were collected using Python 3.11.4 with the google-play-scraper library, extracted from user reviews/comments on each play store page of the top 5 E-commerce platforms in Indonesia. A sampling of 10,000 records was taken to form a long document term matrix (DTM) of 59,981,785 due to the limitation of CPU capacity for data matrix size. R Programming version 4.3.1 was employed for sentiment analysis in this study. It can be concluded that user comments or reviews on the top five (5) E-commerce platforms in Indonesia show positive sentences indicating user satisfaction (3664 sentences), neutral sentences indicating average user appreciation (2282 sentences), and negative sentences indicating user dissatisfaction (4054 sentences). At least with more positive and neutral sentences, it is indicated that 59.64% of E-commerce users in Indonesia express a positive opinion on the performance of the top 5 E-commerce platforms in the country.

Keywords

• e-commerce
• sentiment analysis
• nlp
• text mining
• r programming