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

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