{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:35:03Z","timestamp":1754156103790,"version":"3.41.2"},"reference-count":42,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T00:00:00Z","timestamp":1568160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJWIS"],"published-print":{"date-parts":[[2019,9,11]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The purpose of this paper is to bring together the textual and multimedia opinions, since the use of social data has become the new trend that enables to gather the product reputation traded in social media. Integrating a product reputation process into the companies' strategy will bring several benefits such as helping in decision-making regarding the current and the new generation of the product by understanding the customers\u2019 needs. However, image-centric sentiment analysis has received much less attention than text-based sentiment detection.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>In this work, the authors propose a multimedia content-based product reputation framework that helps in detecting opinions from social media. Thus, in this case, the analysis of a certain publication is made by combining their textual and multimedia parts.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>To test the effectiveness of the proposed framework, a case study based on YouTube videos has been established, as it brings together the image, the audio and the video processing at the same time.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The key novelty is the implication of multimedia content in addition of the textual one with the goal of gathering opinions about a certain product. The multimedia analysis brings together facial sentiment detection, printed text analysis, opinion detection from speeches and textual opinion analysis.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijwis-04-2019-0016","type":"journal-article","created":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T05:45:58Z","timestamp":1587620758000},"page":"95-113","source":"Crossref","is-referenced-by-count":1,"title":["A product reputation framework based on social multimedia content"],"prefix":"10.1108","volume":"16","author":[{"given":"Fatima Zohra","family":"Ennaji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdelaziz","family":"El Fazziki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hasna","family":"El Alaoui El Abdallaoui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Djamal","family":"Benslimane","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Sadgal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020042309454984400_ref002","first-page":"2200","article-title":"SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining","volume-title":"Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC\u201910)","year":"2010"},{"issue":"5","key":"key2020042309454984400_ref003","article-title":"Optical character recognition techniques: a review","volume":"4","year":"2014","journal-title":"International Journal of Advanced Research in Computer Science and Software Engineering"},{"year":"2016","key":"key2020042309454984400_ref004","article-title":"Why you need to go visual in social media in 2016"},{"key":"key2020042309454984400_ref005","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1145\/2502081.2502268","article-title":"Sentibank: large-scale ontology and classifiers for detecting sentiment and emotions in visual content","volume-title":"Proceedings of the 21th ACM international conference on multimedia","year":"2013"},{"key":"key2020042309454984400_ref006","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1145\/2502081.2502282","article-title":"Large-scale visual sentiment ontology and detectors using adjective noun pairs","volume-title":"Proceedings of the 21st ACM international conference on Multimedia \u2013 MM \u201913","year":"2013"},{"key":"key2020042309454984400_ref007","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/978-0-387-74161-1_41","article-title":"Multimodal emotion recognition from expressive faces, body gestures and speech","volume-title":"from book Artificial Intelligence and Innovations 2007: from Theory to Applications: Proceedings of the 4th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2007)","year":"2007"},{"key":"key2020042309454984400_ref008","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1145\/2502081.2502203","article-title":"Understanding and classifying image tweets","volume-title":"Proceeding of ACM international conference on multimedia (MM)","year":"2013"},{"key":"key2020042309454984400_ref009","unstructured":"Co\u00ebff\u00e9, T. 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