{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T14:58:24Z","timestamp":1782313104320,"version":"3.54.5"},"reference-count":57,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T00:00:00Z","timestamp":1656979200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2023,3,17]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>This paper aims to find determinants that can predict the helpfulness of online customer reviews (OCRs) with a novel approach.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>The approach consists of feature engineering using various text mining techniques including BERT and machine learning models that can classify OCRs according to their potential helpfulness. Moreover, explainable artificial intelligence methodologies are used to identify the determinants for helpfulness.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The important result is that the boosting-based ensemble model showed the highest prediction performance. In addition, it was confirmed that the sentiment features of OCRs and the reputation of reviewers are important determinants that augment the review helpfulness.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>Each online community has different purposes, fields and characteristics. Thus, the results of this study cannot be generalized. However, it is expected that this novel approach can be integrated with any platform where online reviews are used.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This paper incorporates feature engineering methodologies for online reviews, including the latest methodology. It also includes novel techniques to contribute to ongoing research on mining the determinants of review helpfulness.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-12-2021-0359","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T13:11:52Z","timestamp":1658149912000},"page":"108-130","source":"Crossref","is-referenced-by-count":13,"title":["Mining the determinants of review helpfulness: a novel approach using intelligent feature engineering and explainable AI"],"prefix":"10.1108","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3733-8702","authenticated-orcid":false,"given":"Jiho","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9005-3661","authenticated-orcid":false,"given":"Hanjun","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4407-0348","authenticated-orcid":false,"given":"Hongchul","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2022,7,5]]},"reference":[{"key":"key2023031608343162100_ref001","first-page":"1287","article-title":"Online review consistency matters: an elaboration likelihood model perspective","volume":"23","year":"2020","journal-title":"Information Systems Frontiers"},{"issue":"3","key":"key2023031608343162100_ref002","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1177\/1470785318819979","article-title":"Predicting the helpfulness of online customer reviews: the role of title features","volume":"62","year":"2020","journal-title":"International Journal of Market Research"},{"key":"key2023031608343162100_ref003","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","year":"2020","journal-title":"Information Fusion"},{"key":"key2023031608343162100_ref004","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","year":"2003","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"key2023031608343162100_ref005","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","year":"2001","journal-title":"Machine Learning"},{"key":"key2023031608343162100_ref006","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.ijhm.2013.08.007","article-title":"New consumer behavior: a review of research on eWOM and hotels","volume":"36","year":"2014","journal-title":"International Journal of Hospitality Management"},{"issue":"2","key":"key2023031608343162100_ref007","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.dss.2010.11.009","article-title":"Exploring determinants of voting for the \u2018helpfulness\u2019 of online user reviews: a text mining approach","volume":"50","year":"2011","journal-title":"Decision Support Systems"},{"issue":"6","key":"key2023031608343162100_ref008","doi-asserted-by":"crossref","first-page":"102266","DOI":"10.1016\/j.ipm.2020.102266","article-title":"Examining the influence of emotional expressions in online consumer reviews on perceived helpfulness","volume":"57","year":"2020","journal-title":"Information Processing & Management"},{"key":"key2023031608343162100_ref009","doi-asserted-by":"crossref","first-page":"113403","DOI":"10.1016\/j.dss.2020.113403","article-title":"An empirical investigation of online review helpfulness: a big data perspective","volume":"139","year":"2020","journal-title":"Decision Support Systems"},{"key":"key2023031608343162100_ref010","article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","year":"2018","journal-title":"ArXiv Preprint"},{"key":"key2023031608343162100_ref011","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.ijhm.2018.07.013","article-title":"What moderates the influence of extremely negative ratings? 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