{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T01:38:07Z","timestamp":1762306687350,"version":"build-2065373602"},"reference-count":48,"publisher":"Emerald","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,6]]},"abstract":"\n Purpose<\/jats:title>\n Market evaluation of products is the basis for product innovation, yet traditional expert-based evaluation methods are highly dependent on the specialization of experts. There exist a lot of weak expert-generated texts on the Internet of their own subjective evaluations of products. Analyzing these texts can indirectly extract the opinions of weak experts and transform them into decision-support information that assists product designers in understanding the market.<\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n In social networks, a subset of users, termed \u201cweak experts\u201d, possess specialized knowledge and frequently share their product experiences online. This study introduces a comparative opinion mining framework that leverages the insights of \u201cweak experts\u201d to analyze user opinions.<\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n An automotive product case study demonstrates that evaluations based on weak expert insights offer managerial insights with a 99.4% improvement in timeliness over traditional expert analyses. Furthermore, in the few-shot sentiment analysis module, with only 10% of the sample, the precision loss is just 1.59%. In addition, the quantitative module of specialization weighting balances low-specialization expert opinions and boosts the weight of high-specialization weak expert views. This new framework offers a valuable tool for companies in product innovation and market strategy development.<\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n This study introduces a novel approach to opinion mining by focusing on the underutilized insights of weak experts. It combines few-shot sentiment analysis with specialization weighting and AHP, offering a comprehensive and efficient tool for product evaluation and market analysis.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/k-02-2024-0318","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T11:16:15Z","timestamp":1718882175000},"page":"6281-6305","source":"Crossref","is-referenced-by-count":2,"title":["Multidimensional product market performance evaluation based on\u00a0a\u00a0weak expert comparative viewpoint mining framework"],"prefix":"10.1108","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6036-359X","authenticated-orcid":true,"given":"Chao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Economics and Management, Tongji University , ,","place":["Shanghai, 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