Sentiment Analysis and Topic Modeling of E-Grocery Application Reviews Using Naive Bayes and Support Vector Machine: A Case Study of Segari Data Review on the Google Play Store

Segari is a customer-centric company with a core value of being obsessed with its customers. The lack of human resources and the abundance of customer reviews that need to be analyzed hinder the process of extracting information from these reviews. Therefore, a machine learning model is needed to automatically perform sentiment analysis and topic modeling. The information extracted from sentiment analysis can be used as a reference to maintain service quality based on positive sentiments, while the results of negative sentiments can be used for evaluation to improve Segari's services and application. The data used on this research were customer reviews from the Google Play Store. The model development process includes data collection, data labeling, data preprocessing, feature extraction, sentiment classification model, model evaluation, and topic modeling. The researcher utilized two classification algorithms, NB and SVM, on a total of 10,507 reviews. The data shows that 74.37% express positive sentiments, while 25.63% express negative sentiments. The results of the study indicate that SVM with oversampling achieved the best model performance, with a recall of 89.98%. Additionally, the researcher used LDA to identify topics related to customer perspectives on Segari, which will be communicated to the relevant team. The analysis revealed that some customers are satisfied while others are disappointed with the product delivery process, application, prices, promotions, and vouchers. Customers generally expressed satisfaction with the quality and freshness of the products. Some customers felt disappointed due to missing or incomplete items in their orders, also to customer service.