dc.description.abstract | Customer review has major impact to the reviewed product maker. Some of which convince
customer whether to buy the product or not. This review also contains a lot meaning which can be
exhausting from the perspective of product’s maker, to understand it one by one. One of the
mediums to access this review is Amazon, which includes many items to be reviewed, either to
check the review or to buy a product. One of the products is Samsung S9 smartphone, which is
discussed by a lot of users on the website. Later, these reviews are downloaded and analysed by
using LDA (Latent Dirichlet Allocation). Kansei engineering is also employed on the analysis to
provide product improvement guideline. These processes will also be held on other reviews dataset
which involve other smartphone brands, to be used as the comparison to the Samsung S9 review
dataset in terms of the findings. The result shows that LDA can summarize the features reviewed
from both dataset such as Bixby feature, Battery life, Picture, Quality, Screen, Camera, Speaker,
etc. While, the Kansei words findings based on the word2vec library implementation showcase
some significant reviews based on the co-occurrence percentage towards the features. Some
examples like the Bixby button feature has Kansei words based on the reviews, such as annoying,
large, edge, etc. The final result is summarized and visualized with product improvement
guidelines which comes from the features and Kansei words analysis from both dataset and
comparison analysis from both results. | en_US |