{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T04:18:01Z","timestamp":1769573881466,"version":"3.49.0"},"reference-count":39,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T00:00:00Z","timestamp":1582156800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["EL"],"published-print":{"date-parts":[[2020,5,13]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>This paper aims to investigate the relationship between sentiment and review helpfulness and develop a method to fully use sentiment features in review helpfulness assessment. In addition, this paper explores whether product type influences evaluating review helpfulness.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>First, a high-quality data set with a manually coded helpfulness score was constructed. Second, detailed research question methods were conducted. Finally, methods were applied to the data set to extract information gain and sentiment scores. Gradient boosting and random forest methods were used to classify the data set with these features through recall, precision and F-measure to understand the research questions.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Review sentiment has a deep relationship with review helpfulness, and it can be a strong predictor of review helpfulness by refining it into more detailed scores; a combination of sentiment scores and information gain works very well on classification for two product types. Product type does not show a significant influence on helpfulness assessment.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This paper provides a different perspective for measuring review sentiment by clarifying the relationship between sentiment and review helpfulness, analysing the role of product type in review helpfulness assessment, and proposing a high-value feature combination. In addition, the author believes that the assessment method can be effectively applied to practical works.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/el-08-2019-0200","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T09:06:03Z","timestamp":1582189563000},"page":"337-351","source":"Crossref","is-referenced-by-count":9,"title":["User review helpfulness assessment based on sentiment analysis"],"prefix":"10.1108","volume":"38","author":[{"given":"Ziming","family":"Zeng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangming","family":"Mu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2020,2,20]]},"reference":[{"issue":"7","key":"key2021102914451768700_ref001","first-page":"16","article-title":"An empirical investigation of self-selection bias and factors influencing review helpfulness","volume":"10","year":"2015","journal-title":"International Journal of Business and Management"},{"issue":"9","key":"key2021102914451768700_ref002","first-page":"65","article-title":"Building a twitter opinion lexicon from automatically-annotated tweets","volume":"108","year":"2016","journal-title":"Knowledge-Based Systems"},{"issue":"3","key":"key2021102914451768700_ref003","doi-asserted-by":"crossref","first-page":"3446","DOI":"10.1016\/j.eswa.2011.09.033","article-title":"An experimental comparison of classification algorithms for imbalanced credit scoring data sets","volume":"39","year":"2012","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"key2021102914451768700_ref004","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1108\/OIR-07-2015-0225","article-title":"Can two-sided messages increase the helpfulness of online reviews?","volume":"40","year":"2016","journal-title":"Online Information Review"},{"issue":"3","key":"key2021102914451768700_ref005","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/TST.2015.7128942","article-title":"Analysis of review helpfulness based on consumer perspective","volume":"20","year":"2015","journal-title":"Tsinghua Science and Technology"},{"issue":"1","key":"key2021102914451768700_ref006","first-page":"547","article-title":"Helpfulness of user-generated reviews as a function of review sentiment, product type and information quality","volume":"54","year":"2016","journal-title":"Computers in Human Behavior"},{"issue":"4","key":"key2021102914451768700_ref007","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1086\/209370","article-title":"An integrative framework for understanding two-sided persuasion","volume":"20","year":"1994","journal-title":"Journal of Consumer Research"},{"issue":"2","key":"key2021102914451768700_ref008","first-page":"65","article-title":"Adapting sentiment lexicons to domain-specific social media texts","volume":"94","year":"2017","journal-title":"Decision Support Systems"},{"issue":"10","key":"key2021102914451768700_ref009","first-page":"3133","article-title":"Do we need hundreds of classifiers to solve real world classification problems?","volume":"15","year":"2014","journal-title":"Journal of Machine Learning Research"},{"issue":"6","key":"key2021102914451768700_ref010","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1016\/j.jbusres.2014.11.006","article-title":"What makes online reviews helpful? 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