Sentiment Analysis Of E-Commerce Product Reviews For Content Interaction Using Machine Learning

Abstract

With the growth of various e-commerce applications, it has become increasingly important to understand consumer spending patterns and provide products that best respond to their needs. One way of doing this is by utilizing the data available in consumer product reviews to improve the level of content interaction. I
 n order to tap into this data, companies can utilize a process called sentiment analysis to identify consumers’ sentiments or reactions towards certain products. This study proposes to compare different methods of sentiment analysis focused on one specific product, utilizing a machine learning approach with Naïve Bayes and Support Vector Machine classifiers with the aim of finding which method produces the best results in terms of accuracy, precision, recall and F-measure. A specific focus has been made to analyze the consumer reviews of wireless earphone products from the Indonesian e-commerce company Tokopedia. The results of this study show that utilizing the Naïve Bayes classifier, enhanced with hyperparameter tuning produced the best results in terms of accuracy, recall, and F-measure with a value of 77.03%, 73.03%, and 74.41% respectively. Whereas the best precision was obtained by utilizing an SVM classifier enhanced with hyperparameter tuning, producing a value of 76.05%.