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Cited by 66Sentiment Analysis and Opinion MiningDigital Marketing and Social MediaExpert finding and Q&A systems

Identifying helpful reviews based on customer’s mentions about experiences

Min, Hye-Jin, Park, Jong C.·2012·Expert Systems with Applications
DOI 10.1016/j.eswa.2012.01.116
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Crossref · 10.1016/j.eswa.2012.01.116

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  • It remains unknown how reviews can help with the unique interests of BLV users, especially providing descriptive information on product appearance.

    “Prior work towards general users investigated extracting experiences [21, 24], tips [11, 41] or snippets suitable for product descriptions [7, 25], yet it remains unknown how reviews can help with the unique interests of BLV users, especially providing descriptive information on product appearance.”

    From Revamp: Enhancing Accessible Information Seeking Experience of Online Shopping for Blind or Low Vision Users (2021) · cites this paper as [21]

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Authors
Min, H.-J., & Park, J. C.
Affiliations
Korea Advanced Institute of Science and Technology (KR)
Corresponding author
Park, Jong C.
ORCID
Park, Jong C.
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2012-11
Volume
39
Issue
15
Pages
11830-11838
ISSN
0957-4174 (ISSN-L)
DOI
10.1016/j.eswa.2012.01.116
OpenAlex
W2046122205
Cited by
66
References
1
Citations by year
2026: 1 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 4 · 2021: 6 · 2020: 7 · 2019: 8 · 2018: 6 · 2017: 5 · 2016: 2 · 2015: 6 · 2014: 4 · 2013: 6 · 2012: 1
FWCI
5.35
Type
journal article
Language
en
Open access
closed
Licence
Locations
Expert Systems with Applications (publishedVersion)
Funders
  • National Research Foundation of Korea
  • Ministry of Education, Science and Technology (2011-0004422)
  • Ministry of Trade, Industry and Energy
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
  • Physical Sciences › Computer Science › Information Systems › Expert finding and Q&A systems
Keywords
online customer reviews, review quality assessment, review ranking, temporal expressions, linguistic cues, recency bias, popularity bias, usability testing

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