Application of Sentiment Analysis on Product Review E-Commerce

Abstract

The lack of buying product via online are the consumer cannot touch, try, or even see it directly. Then how does the consumer believe the product they like, is the correct product to be bought. The main key is product review from the consumer who have bought and try the product. The more number of product review on certain product or the popular product caused difficulties for the consumer to decide which product they should choose. For that we require a solution, which is the writer will apply sentiment analysis to classify every product review into positive orientation, negative or neutral and also produce summary of product review based on product feature to help reading process, product review and decision making. The writer step is (1) data collecting and preprocessing, (2) product feature extraction using Double Propagation, (3) deciding sentiment orientation, (4) classifying by using Naïve Bayes Classifier and Support Vector Machine, and (5) summary generation based on product feature. These stages run in a simulator and the result of the classification from both methods compared.