An automated method to conduct importance-performance analysis of product attributes from online reviews - An extension with a case study

Kangcheng Lin, Harrison Kim

Research output: Contribution to journalConference articlepeer-review

Abstract

With the growth of online marketplaces and social media, product designers have been seeing an exponential growth of data available, which can serve as an extremely valuable source of information communicated from customers without geographical limitations. The data will reveal customers’ preferences, which can be expensive and slow to obtain via traditional methods such as survey and questionnaires. While existing methods in the literature have been proposed to extract product information and make inference from online data, they have limitations, especially in providing reliable results and in dealing with data sparsity. Therefore, this paper proposes a method to conduct an Important-performance analysis from online reviews. The major steps of this method involve using latent Dirichlet allocation (LDA) to identify product attributes, using IBM Watson Natural Language Understanding tool to perform aspect-based sentiment analysis, and using XGBoost model to infer product attribute importance from the collected dataset. In our case study, we have collected over 150,000 text reviews of more than 3,000 laptops from Amazon.

Original languageEnglish (US)
Pages (from-to)417-426
Number of pages10
JournalProceedings of the Design Society
Volume1
DOIs
StatePublished - 2021
Event23rd International Conference on Engineering Design, ICED 2021 - Gothenburg, Sweden
Duration: Aug 16 2021Aug 20 2021

Keywords

  • Big data
  • Market implications
  • Semantic data processing

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Software
  • Modeling and Simulation

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