Implementasi Algoritma Naïve Bayes dalam Menganalisis Sentimen Review Pengguna Tokopedia pada Produk Kesehatan

Abstract

It must be realized that customer satisfaction is the main goal for companies in developing their business. Because customers' opinions written on social media will have a big influence on the company and potential customers. In its development, it is increasingly found in various online media, one of which is Tokopedia. Product reviews are an important source of information regarding quality, service and delivery from both consumers and manufacturers. With a very large amount of data for each product on Tokopedia, analyzing and concluding product review information will definitely take a lot of time if done manually. To overcome this, a sentiment analysis system is needed that can automatically extract important information that can objectively determine product quality and handle large amounts of textual information. The sentiment analysis system consists of several stages, namely crawling, pre-processing, word weighting, and sentiment classification. By applying the Naïve Bayes algorithm through selecting range and frequency features, accuracy, accuracy and recall results will be obtained using the Confusion Matrix test. The dataset used is from the kaggle.com site regarding customer sentiment on health products with the type of mask. using the Naïve Bayes Algorithm Method to determine the sentiment of user reviews by classifying 2 positive and negative classes using the NLP approach produces an accuracy value of 88%.