Sentiment Analysis of Customer Reviews Using Support Vector Machine and Smote-Tomek Links For Identify Customer Satisfaction

Abstract

Shopping activities in the online market, especially fashion trends, continue to increase with all the promo efforts offered. One of the considerations for buying products on the online market is to read reviews. Each consumer review shows the level of interest in the product. The number of negative reviews and the emergence of many varied reviews pose a problem in categorizing reviews. Sentiment analysis is a way of looking at the polarity of reviews to classify positive and negative reviews. The Support Vector Machine method and the combination of the Synthetic Minority Oversampling Technique (SMOTE) with Tomek Links are applied in this study. Classification using the Support Vector Machine method and the combination of the Synthetic Minority Oversampling Technique (SMOTE) with Tomek Links showed better results with an Accuracy of 0.92, Precision of 0.89, Recall of 0.89, and F1-score of 0.89 than without the combination of the Synthetic Minority oversampling Technique (SMOTE) with Tomek Links with an Accuracy of 0.68, Precision of 0.55, Recall of 0.99, and an F1-score of 0.71.