Sentiment Analysis on Medical Personal Protective Equipment (PPE) Shops Customer Reviews

ABSTRACT

Online shopping through e-commerce sites is becoming more prevalent with the expansion towards a more digital age in our society together with recent factors such that of the COVID-19 Pandemic. Through Machine Learning and the concept of Sentiment Analysis, algorithms would be able to identify the sentiment of reviewers by processing the words used in the sentence. The research aims to determine the reliability of star ratings compared to sentiment analysis and which classification algorithm suits best for text classification by Filipino customer reviews in Shopee for Medical Personal Protective Equipment or PPEs. It also aims to identify the best classifier model to use in terms of its performance. The study was divided into two models star ratings and sentiment analysis. Both data sets performed different preprocessing techniques and tested for Naive Bayes and Support Vector Machine classification models, and their performance measures were obtained. The findings of the study show that star ratings and annotated reviews present high similarity in terms of the sentiment and polarity classified per review. In terms of the best performing model, Support Vector Machine achieved the best scores for the performance measures among the tests. © 2022 IEEE.