Automated keyword filtering in latent dirichlet allocation for identifying product attributes from online reviews

Junegak Joung, Harrison M. Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Identifying product attributes from the perspective of a customer is essential to measure the satisfaction, importance, and Kano category of each product attribute for product design. This article proposes automated keyword filtering to identify product attributes from online customer reviews based on latent Dirichlet allocation. The preprocessing for latent Dirichlet allocation is important because it affects the results of topic modeling; however, previous research performed latent Dirichlet allocation either without removing noise keywords or by manually eliminating them. The proposed method improves the preprocessing for latent Dirichlet allocation by conducting automated filtering to remove the noise keywords that are not related to the product. A case study of Android smartphones is performed to validate the proposed method. The performance of the latent Dirichlet allocation by the proposed method is compared to that of a previous method, and according to the latent Dirichlet allocation results, the former exhibits a higher performance than the latter.

Original languageEnglish (US)
Article number084501
JournalJournal of Mechanical Design, Transactions of the ASME
Volume143
Issue number8
DOIs
StatePublished - Aug 2021

Keywords

  • Design automation

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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