{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T23:34:45Z","timestamp":1776468885697,"version":"3.51.2"},"reference-count":39,"publisher":"Emerald","issue":"8","license":[{"start":{"date-parts":[[2014,9,2]],"date-time":"2014-09-02T00:00:00Z","timestamp":1409616000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,9,2]]},"abstract":"\n Purpose<\/jats:title>\n \u2013 Extant methods of product weakness detection usually depend on time-consuming questionnaire with high artificial involvement, so the efficiency and accuracy are not satisfied. The purpose of this paper is to propose an opinion-aware analytical framework \u2013 PRODWeakFinder \u2013 to expect to detect product weaknesses through sentiment analysis in an effective way. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 PRODWeakFinder detects product weakness by considering both comparative and non-comparative evaluations in online reviews. For comparative evaluation, an aspect-oriented comparison network is built, and the authority is assessed for each node by network analysis. For non-comparative evaluation, sentiment score is calculated through sentiment analysis. The composite score of aspects is calculated by combing the two types of evaluations. <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 The experiments show that the comparative authority score and the non-comparative sentiment score are not highly correlated. It also shows that PRODWeakFinder outperforms the baseline methods in terms of accuracy. <\/jats:p>\n <\/jats:sec>\n \n Research limitations\/implications<\/jats:title>\n \u2013 Semantic-based method such as ontology are expected to be applied to identify the implicit features. Furthermore, besides PageRank, other sophisticated network algorithms such as HITS will be further employed to improve the framework. <\/jats:p>\n <\/jats:sec>\n \n Practical implications<\/jats:title>\n \u2013 The link-based network is more suitable for weakness detection than the weight-based network. PRODWeakFinder shows the potential on reducing overall costs of detecting product weaknesses for companies. <\/jats:p>\n <\/jats:sec>\n \n Social implications<\/jats:title>\n \u2013 A quicker and more effective way would be possible for weakness detection, enabling to reduce product defects and improve product quality, and thus raising the overall social welfare. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 An opinion-aware analytical framework is proposed to sentiment mining of online product reviews, which offer important implications regarding how to detect product weaknesses.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/imds-05-2014-0159","type":"journal-article","created":{"date-parts":[[2014,9,17]],"date-time":"2014-09-17T08:15:07Z","timestamp":1410941707000},"page":"1301-1320","source":"Crossref","is-referenced-by-count":43,"title":["Product weakness finder: an opinion-aware system through sentiment analysis"],"prefix":"10.1108","volume":"114","author":[{"given":"Hongwei","family":"Wang","sequence":"first","affiliation":[]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020122902171168400_b1","doi-asserted-by":"crossref","unstructured":"Archak, N.\n , \n Ghose, A.\n and \n Ipeirotis, P.G.\n (2011), \u201cDeriving the pricing power of product features by mining consumer reviews\u201d, Management Science, Vol. 57 No. 8, pp. 1485-1509.","DOI":"10.1287\/mnsc.1110.1370"},{"key":"key2020122902171168400_b2","doi-asserted-by":"crossref","unstructured":"Basiri, M.E.\n , \n Ghasem-Aghaee, N.\n and \n Naghsh-Nilchi, A.R.\n (2014), \u201cExploiting reviewers\u2019 comment histories for sentiment analysis\u201d, Journal of Information Science, Vol. 40 No. 3, pp. 313-328.","DOI":"10.1177\/0165551514522734"},{"key":"key2020122902171168400_b3","doi-asserted-by":"crossref","unstructured":"Brin, S.\n and \n Page, L.\n (1998), \u201cThe anatomy of a large-scale hypertextual web search engine\u201d, Computer Networks and ISDN Systems, Vol. 30 No. 1, pp. 107-117.","DOI":"10.1016\/S0169-7552(98)00110-X"},{"key":"key2020122902171168400_b4","doi-asserted-by":"crossref","unstructured":"Bruce, R.F.\n and \n Wiebe, J.M.\n (1999), \u201cRecognizing subjectivity: a case study in manual tagging\u201d, Natural Language Engineering, Vol. 5 No. 2, pp. 187-205.","DOI":"10.1017\/S1351324999002181"},{"key":"key2020122902171168400_b5","doi-asserted-by":"crossref","unstructured":"Chen, C.C.\n , \n Chen, Z.Y.\n and \n Wu, C.Y.\n (2012a), \u201cAn unsupervised approach for person name bipolarization using principal component analysis\u201d, Knowledge and Data Engineering, IEEE Transactions on, Vol. 24 No. 11, pp. 1963-1976.","DOI":"10.1109\/TKDE.2011.177"},{"key":"key2020122902171168400_b6","doi-asserted-by":"crossref","unstructured":"Chen, L.\n , \n Qi, L.\n and \n Wang, F.\n (2012b), \u201cComparison of feature-level learning methods for mining online consumer reviews\u201d, Expert Systems with Applications, Vol. 39 No. 10, pp. 9588-9601.","DOI":"10.1016\/j.eswa.2012.02.158"},{"key":"key2020122902171168400_b7","doi-asserted-by":"crossref","unstructured":"Darena, F.\n and \n Burda, K.\n (2012), \u201cGrouping of customer opinions written in natural language using unsupervised machine learning\u201d, Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2012 14th International Symposium on IEEE, West University, Timisoara, pp. 265-270.","DOI":"10.1109\/SYNASC.2012.29"},{"key":"key2020122902171168400_b8","doi-asserted-by":"crossref","unstructured":"Decker, R.\n and \n Trusov, M.\n (2010), \u201cEstimating aggregate consumer preferences from online product reviews\u201d, International Journal of Research in Marketing, Vol. 27 No. 4, pp. 293-307.","DOI":"10.1016\/j.ijresmar.2010.09.001"},{"key":"key2020122902171168400_b9","doi-asserted-by":"crossref","unstructured":"Ding, X.\n , \n Liu, B.\n and \n Yu, P.S.\n (2008), \u201cA holistic lexicon-based approach to opinion mining\u201d, Proceedings of the International Conference on Web Search and Web Data Mining. 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