A Machine Learning Approach to Analyze Fashion Styles from Large Collections of Online Customer Reviews

Hananto, Valentinus Roby ORCID: https://orcid.org/0000-0003-1988-3168, Kim, Soomin, Kovacs, Mate, Serdült, Uwe and Kryssanov, Victor (2021) A Machine Learning Approach to Analyze Fashion Styles from Large Collections of Online Customer Reviews. In: International Conference on Business and Industrial Research (ICBIR). Thai-Nichi Institute of Technology, Bangkok, Thailand, pp. 153-158. ISBN 978-1-6654-3349-5

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Abstract

Social media and online reviews have changed customer behavior when buying fashion products online. Online customer reviews also provide opportunities for businesses to deliver improved customer experiences. This study aims to develop fashion style models, based on online customer reviews from e-commerce systems to analyze customer preferences. Topic Modeling with Latent Dirichlet Allocation (LDA) was performed on a large collection of online customer reviews in different categories to investigate customer preferences by building fashion style models in a semantic space. Online product review data from Amazon, one of the leading online shopping websites globally, and Rakuten, one of the representative online shopping websites in Japan, were used to reveal the hidden topics in the review texts. The obtained topic definitions were manually examined, and the results were used to build computational models reflecting semantic relationships. The obtained fashion style models can potentially help marketing and product design specialists better understand customer preferences in the e-commerce fashion industry.


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Item Type: Book Section
Additional Information: https://doi.org/10.1109/ICBIR52339.2021.9465830
Uncontrolled Keywords: Analytical models, Annotations, Social networking (online), Navigation, Computational modeling, Semantics, Product design
Dewey Decimal Classification: 000 - Computer science, information & general works > 000 Computer science, knowledge & systems > 005 Computer programming, programs & data
Divisions: Perpustakaan > Prosiding/Call for Papers
Depositing User: Agung P. W.
Date Deposited: 26 Jul 2022 15:40
Last Modified: 26 Jul 2022 15:40
URI: http://repository.dinamika.ac.id/id/eprint/6525

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