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.
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- Industrial Engineering [2224]