Comparison of Naïve Bayes Algorithm and XGBoost on Local Product Review Text Classification

Online reviews are critical in supporting purchasing decisions because, with the development of e-commerce, there are more and more fake reviews, so more and more consumers are worried about being deceived in online shopping. Sentiment analysis can be applied to Marketplace product reviews. This study aims to compare the two categories of Naïve Bayes and XGBoost by using the two vector spaces wod2vec and TFIDF. The methods used in this research are data collection, data cleaning, data labelling, data pre-processing, classification and evaluation. The data scraping process produced 25,581 data which was divided into 80% training data and 20% test data. The data is divided into two classes, namely good sentiment and bad sentiment. Based on the research that has been done, the combination of Word2vec + XGBoost F1 scores higher by 0.941, followed by TF-IDF + XGBoost by 0.940. Meanwhile, Naïve Bayes has an F1-Score of 0.915 with TF-IDF and 0.900 with word2vec. Classification using XGBoost proved to be able to classify unbalanced data better than Naïve Bayes.