{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T12:56:10Z","timestamp":1760100970378,"version":"3.41.2"},"reference-count":43,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2017,5,8]],"date-time":"2017-05-08T00:00:00Z","timestamp":1494201600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2017,5,8]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Comparative opinions widely exist in online reviews as a common way of expressing consumers\u2019 ideas or preferences toward certain products. Such opinion-rich texts are key proxies for detecting product competitiveness. The purpose of this paper is to set up a model for competitiveness analysis by identifying comparative relations from online reviews for restaurants based on both pattern matching and machine learning.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The authors define the sub-category of comparative sentences according to Chinese linguistics. Classification rules are set up for each type of comparative relations through class sequence rule. To improve the accuracy of classification, a comparative entity dictionary is then introduced for further identifying comparative sentences. Finally, the authors collect reviews for restaurants from Dianping.com to conduct experiments for testing the proposed model.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The experiments show that the proposed method outperforms the baseline methods in terms of precision in identifying comparative sentences. On the basis of such comparison-rich sentences, product features and comparative relations are extracted for sentiment analysis, and sentimental score is assigned to each comparative relation to facilitate competitiveness analysis.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>Only the explicit comparative relations are discussed, neglecting the implicit ones. Besides that, the study is grounded in the assumption that all features are homogeneous. In some cases, however, the weights to different aspects are not of the same importance to market.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>On the basis of comparative relation mining, product features and comparative opinions are extracted for competitiveness analysis, which is of interest to businesses for finding weakness or strength of products, as well as to consumers for making better purchase decisions.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Social implications<\/jats:title>\n<jats:p>Comparative relation mining could be possibly applied in social media for identifying relations among users or products, and ranking users or products, as well as helping companies target and track competitors to enhance competitiveness.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The authors propose a research framework for restaurant competitiveness analysis by mining comparative relations from online consumer reviews. The results would be able to differentiate one restaurant from another in some aspects of interest to consumers, and reveal the changes in these differences over time.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/imds-07-2016-0284","type":"journal-article","created":{"date-parts":[[2017,3,20]],"date-time":"2017-03-20T20:23:40Z","timestamp":1490041420000},"page":"672-687","source":"Crossref","is-referenced-by-count":24,"title":["Competitiveness analysis through comparative relation mining"],"prefix":"10.1108","volume":"117","author":[{"given":"Hongwei","family":"Wang","sequence":"first","affiliation":[]},{"given":"Song","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Pei","family":"Yin","sequence":"additional","affiliation":[]},{"given":"James Nga-Kwok","family":"Liu","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"issue":"1","key":"key2020120718523526800_ref001","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.eswa.2011.06.045","article-title":"Facility layout using weighted association rule-based data mining algorithms: evaluation with simulation","volume":"39","year":"2012","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"key2020120718523526800_ref002","first-page":"249","article-title":"Analysis of patent documents with weighted association rules","volume":"92","year":"2015","journal-title":"Technological Forecasting and Social Change"},{"issue":"3","key":"key2020120718523526800_ref003","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1504\/IJISE.2013.056676","article-title":"Fuzzy weighted association rule based solution approaches to facility layout problem in cellular manufacturing system","volume":"15","year":"2013","journal-title":"International Journal of Industrial and Systems Engineering"},{"issue":"3","key":"key2020120718523526800_ref004","first-page":"358","article-title":"Product opinion mining for competitive intelligence","volume":"73","year":"2015","journal-title":"Procedia Computer Science"},{"issue":"8","key":"key2020120718523526800_ref005","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1287\/mnsc.1110.1370","article-title":"Deriving the pricing power of product features by mining consumer reviews","volume":"57","year":"2011","journal-title":"Management Science"},{"issue":"2","key":"key2020120718523526800_ref006","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s10489-016-0768-0","article-title":"Exploring sentiment parsing of microblogging texts for opinion polling on Chinese public figures","volume":"45","year":"2016","journal-title":"Applied Intelligence"},{"year":"2005","first-page":"169","article-title":"Semantic role labelling with tree conditional random fields","key":"key2020120718523526800_ref007"},{"issue":"2","key":"key2020120718523526800_ref008","first-page":"285","article-title":"Combining similarity and sentiment in opinion mining for product recommendation","volume":"46","year":"2015","journal-title":"Journal of Intelligent Information Systems"},{"issue":"4","key":"key2020120718523526800_ref009","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1145\/2436256.2436274","article-title":"Techniques and applications for sentiment analysis","volume":"56","year":"2013","journal-title":"Communications of the ACM"},{"issue":"7","key":"key2020120718523526800_ref010","first-page":"186","article-title":"Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon","volume":"37","year":"2013","journal-title":"Knowledge-Based Systems"},{"year":"2008","first-page":"241","article-title":"Mining opinions in comparative sentences","key":"key2020120718523526800_ref011"},{"year":"2004","first-page":"1","article-title":"Semantic role labeling using dependency trees","key":"key2020120718523526800_ref012"},{"doi-asserted-by":"crossref","unstructured":"He, S., Yuan, F. and Wang, Y. 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