Aspect Based Sentiment Analysis For Extracting Kansei Word Using Spacy Library (A Case Study On Smartphone Product)

Abstract

Conventional Kansei engineering is has challenging to conduct. Mostly, Kansei engineering is conducted by employing a questionnaire, survey, or interview. That method needs lot of time to be done and from the previous research is only a few data was acquired. Currently, data availability on the internet such as product reviews is easily found on the online marketplace. Product review from customers has drawn special attention from the product owner. Product review can convince the customer to buy the product or find other products. One of the platforms that provide the review accessibility is Amazon. Amazon can provide a lot of product reviews generated from the user that buy products from Amazon. One of the products is the Samsung Galaxy S9, which is reviewed a lot by the user. This paper discussed aspect-based sentiment analysis for Kansei engineering. The main advantage of this method, it can process larger data in a short time with the help of a programming tool. It can be used to analyze the product review that eventually can be used by the product owner to identify what the customer says about their product. Those reviews are gathered using scrapper and analyzed using SPACY library that employs machine learning to do the analysis.