The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Automated Keyword Filtering in Latent Dirichlet Allocation for Identifying Product Attributes From Online Reviews
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.
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“To mitigate these risks, employing keyword filtering and discriminative models may effectively prevent the inclusion of such inappropriate data [168, 169].”
From Human-inspired Perspectives: A Survey on AI Long-term Memory (2024) · cites this paper as [169]
Citations
Cites
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Normality Tests for Statistical Analysis: A Guide for Non-Statisticians
Ghasemi, A., & Zahediasl, S. (2012) · International Journal of Endocrinology and Metabolism · doi:10.5812/ijem.3505 · Cited by 4,368
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Cited by
Human-inspired Perspectives: A Survey on AI Long-term Memory
He, Z., Lin, W., Zheng, H., Zhang, F., Jones, M. W., Aitchison, L., Xu, X., Liu, M., Kristensson, P. O., & Shen, J. (2024) · arXiv · doi:10.48550/arxiv.2411.00489 · Cited by 3
“Furthermore, compared to non-parametric memory, the characteristics of parametric memory storing information implicitly within AI models are more akin to how the human brain produces new neurons or undergoes chemical changes [84, 86, 87, 88].”
Metadata
- Authors
- Joung, J., & Kim, H. M.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Venue
- Journal of Mechanical Design
- Publisher
- ASME International
- Published
- 2021-02-09
- Volume
- 143
- Issue
- 8
- ISSN
- 0161-8458 (ISSN-L)
- 1050-0472
- 1528-9001
- OpenAlex
- W3096554474
- Cited by
- 68
- References
- 2
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 15 · 2023: 18 · 2022: 14 · 2021: 6
- FWCI
- 3.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Mechanical Design (publishedVersion)
- Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
- Funders
- National Research Foundation of Korea (2019R1I1A1A01063298)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- keyword filtering, latent Dirichlet allocation, topic modeling, online customer reviews, text preprocessing, Android smartphones, Kano model, customer satisfaction
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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