ENSEMBLE LEARNING DAN ANALISIS SENTIMEN PADA DATA ULASAN PRODUK

Abstract

The majority of internet users are currently searching the internet before buying certain products. One consideration of prospective buyers is product reviews (product review). Prospective consumers can decide to buy a product because it is influenced by reviews with positive sentiments, or decide not to buy a particular product because it is influenced by a negative sentiment review. Product reviews are a way of delivering consumer opinions and sentiments to a product online. In essence, the product review data mined directly from the database is unbalanced, between positive sentiment and negative sentiment. This condition makes it difficult for machine learning algorithms to perform classification and clustering functions. In this study, sentiment analysis was conducted based on Trendy Shoes products from Denim Shoes. The stages of sentiment analysis consist of data collection, initial processing, data transformation, feature selection and classification stages using SMOTEBoost. Initial processing applies the stages of text mining namely case folding, non alpha numeric removal, stop words removal, and stemming. The results of sentiment analysis were measured using the criteria of Accuracy, G-Mean, and F-Measure. By applying the test to two types of sentiment data, the results show that SMOTEBoost can classify sentiments well. SMOTEBoost's performance is compared to other ensemble techniques namely ADABoost, RUSBoost, and SMOTEBagging. Classification results of review_1 data, SMOTEboost is better in accuracy and G-Mean. While for the review_2 data, SMOTEBoost has better results for all criteria, both accuracy, F-Measure and G-Mean