{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:49:13Z","timestamp":1711673353581},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,27]],"date-time":"2024-01-27T00:00:00Z","timestamp":1706313600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,27]],"date-time":"2024-01-27T00:00:00Z","timestamp":1706313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Multimed Info Retr"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s13735-023-00314-4","type":"journal-article","created":{"date-parts":[[2024,1,27]],"date-time":"2024-01-27T10:02:23Z","timestamp":1706349743000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Opinion convergence-based sentiment prediction of image advertisement"],"prefix":"10.1007","volume":"13","author":[{"given":"Younghoon","family":"Lee","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,27]]},"reference":[{"key":"314_CR1","doi-asserted-by":"crossref","unstructured":"Achlioptas P, Ovsjanikov M, Haydarov K, Elhoseiny M, Guibas LJ (2021) Artemis: affective language for visual art. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11569\u201311579","DOI":"10.1109\/CVPR46437.2021.01140"},{"issue":"1","key":"314_CR2","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1002\/arcp.1049","volume":"2","author":"R Adaval","year":"2019","unstructured":"Adaval R, Saluja G, Jiang Y (2019) Seeing and thinking in pictures: a review of visual information processing. Consum Psychol Rev 2(1):50\u201369","journal-title":"Consum Psychol Rev"},{"key":"314_CR3","doi-asserted-by":"crossref","unstructured":"Asakawa T, Aono M (2021) Multi-label prediction for visual sentiment analysis using eight different emotions based on psychology. In: Proceedings of the 4th international conference on control and computer vision, pp 142\u2013146","DOI":"10.1145\/3484274.3484296"},{"key":"314_CR4","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1007\/s13042-017-0734-0","volume":"10","author":"S Corchs","year":"2019","unstructured":"Corchs S, Fersini E, Gasparini F (2019) Ensemble learning on visual and textual data for social image emotion classification. Int J Mach Learn Cybern 10:2057\u20132070","journal-title":"Int J Mach Learn Cybern"},{"issue":"14","key":"314_CR5","doi-asserted-by":"publisher","first-page":"2407","DOI":"10.1080\/0144929X.2022.2126329","volume":"42","author":"B Ghani","year":"2022","unstructured":"Ghani B, Malik MAR (2022) Social media and employee voice: a comprehensive literature review. Behav Inf Technol 42(14):2407\u20132427","journal-title":"Behav Inf Technol"},{"key":"314_CR6","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"314_CR7","doi-asserted-by":"crossref","unstructured":"Hussain Z, Zhang M, Zhang X, Ye K, Thomas C, Agha Z, Ong N, Kovashka A (2017) Automatic understanding of image and video advertisements. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1705\u20131715","DOI":"10.1109\/CVPR.2017.123"},{"issue":"6","key":"314_CR8","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.3390\/app9061123","volume":"9","author":"M Jabreel","year":"2019","unstructured":"Jabreel M, Moreno A (2019) A deep learning-based approach for multi-label emotion classification in tweets. Appl Sci 9(6):1123","journal-title":"Appl Sci"},{"key":"314_CR9","doi-asserted-by":"crossref","unstructured":"Jia Z, Narayana P, Akula AR, Pruthi G, Su H, Basu S, Jampani V (2023) Kafa: rethinking image ad understanding with knowledge-augmented feature adaptation of vision-language models. arXiv preprint arXiv:2305.18373","DOI":"10.18653\/v1\/2023.acl-industry.74"},{"issue":"5","key":"314_CR10","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1109\/MSP.2011.941851","volume":"28","author":"D Joshi","year":"2011","unstructured":"Joshi D, Datta R, Fedorovskaya E, Luong QT, Wang JZ, Li J, Luo J (2011) Aesthetics and emotions in images. IEEE Signal Process Mag 28(5):94\u2013115","journal-title":"IEEE Signal Process Mag"},{"issue":"3","key":"314_CR11","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1080\/00913367.2019.1597787","volume":"48","author":"BK Kim","year":"2019","unstructured":"Kim BK, Choi J, Wakslak CJ (2019) The image realism effect: the effect of unrealistic product images in advertising. J Advert 48(3):251\u2013270","journal-title":"J Advert"},{"key":"314_CR12","unstructured":"Kingma D, Ba J (2015) Adam: a method for stochastic optimization. In: Proceedings of the 3rd international conference for learning representations (iclr\u201915), San Diego, vol 500"},{"issue":"1","key":"314_CR13","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1177\/1356766718757272","volume":"25","author":"S Kong","year":"2019","unstructured":"Kong S, Huang Z, Scott N, Zhang Z, Shen Z (2019) Web advertisement effectiveness evaluation: attention and memory. J Vacat Mark 25(1):130\u2013146","journal-title":"J Vacat Mark"},{"key":"314_CR14","doi-asserted-by":"crossref","unstructured":"Kujur F, Singh S (2020) Visual communication and consumer-brand relationship on social networking sites-uses & gratifications theory perspective. J Theor Appl Electron Commer Res 15(1):30\u201347","DOI":"10.4067\/S0718-18762020000100104"},{"key":"314_CR15","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1609\/aaai.v37i1.25076","volume":"37","author":"Y Kumar","year":"2023","unstructured":"Kumar Y, Jha R, Gupta A, Aggarwal M, Garg A, Malyan T, Bhardwaj A, Shah RR, Krishnamurthy B, Chen C (2023) Persuasion strategies in advertisements. Proceedings of the AAAI conference on artificial intelligence 37:57\u201366","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"key":"314_CR16","doi-asserted-by":"crossref","unstructured":"Li L, Zhu X, Hao Y, Wang S, Gao X, Huang Q (2019) A hierarchical CNN-RNN approach for visual emotion classification. ACM Trans Multimedia Comput, Commun, Appl (TOMM) 15(3s):1-17","DOI":"10.1145\/3359753"},{"key":"314_CR17","doi-asserted-by":"publisher","first-page":"2661","DOI":"10.1609\/aaai.v34i03.5651","volume":"34","author":"C Lin","year":"2020","unstructured":"Lin C, Zhao S, Meng L, Chua TS (2020) Multi-source domain adaptation for visual sentiment classification. Proceedings of the AAAI conference on artificial intelligence 34:2661\u20132668","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"key":"314_CR18","doi-asserted-by":"crossref","unstructured":"Pilli S, Patwardhan M, Pedanekar N, Karande S (2020) Predicting sentiments in image advertisements using semantic relations among sentiment labels. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp 408\u2013409","DOI":"10.1109\/CVPRW50498.2020.00212"},{"issue":"1","key":"314_CR19","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1080\/00913367.2019.1579688","volume":"48","author":"K Poels","year":"2019","unstructured":"Poels K, Dewitte S (2019) The role of emotions in advertising: a call to action. J Advert 48(1):81\u201390","journal-title":"J Advert"},{"key":"314_CR20","doi-asserted-by":"crossref","unstructured":"Ruan S, Zhang K, Wang Y, Tao H, He W, Lv G, Chen E (2020) Context-aware generation-based net for multi-label visual emotion recognition. In: 2020 IEEE international conference on multimedia and expo (ICME). IEEE Computer Society, pp 1\u20136","DOI":"10.1109\/ICME46284.2020.9102855"},{"key":"314_CR21","unstructured":"Sanggar N (2022) The effectiveness of interactive stickers used on instagram stories in influencing generation z\u2019s perception towards brands: a study on the persuasiveness of visual communication on social media. PhD thesis, Tunku Abdul Rahman University College"},{"issue":"4","key":"314_CR22","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1080\/00913367.1991.10673351","volume":"20","author":"RA Smith","year":"1991","unstructured":"Smith RA (1991) The effects of visual and verbal advertising information on consumers\u2019 inferences. J Advert 20(4):13\u201324","journal-title":"J Advert"},{"key":"314_CR23","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.neucom.2018.05.104","volume":"312","author":"K Song","year":"2018","unstructured":"Song K, Yao T, Ling Q, Mei T (2018) Boosting image sentiment analysis with visual attention. Neurocomputing 312:218\u2013228","journal-title":"Neurocomputing"},{"key":"314_CR24","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR, pp 6105\u20136114"},{"key":"314_CR25","doi-asserted-by":"crossref","unstructured":"Yang J, Sun M, Sun X (2017) Learning visual sentiment distributions via augmented conditional probability neural network. In: Proceedings of the 31st AAAI conference on artificial intelligence, AAAI\u201917. AAAI Press, p 224\u2013230","DOI":"10.1609\/aaai.v31i1.10485"},{"key":"314_CR26","doi-asserted-by":"crossref","unstructured":"Zhang H, Luo Y, Ai Q, Wen Y, Hu H (2020) Look, read and feel: Benchmarking ads understanding with multimodal multitask learning. In: Proceedings of the 28th ACM international conference on multimedia, pp 430\u2013438","DOI":"10.1145\/3394171.3413582"},{"key":"314_CR27","doi-asserted-by":"publisher","first-page":"2203","DOI":"10.1109\/TMM.2022.3144804","volume":"25","author":"H Zhang","year":"2022","unstructured":"Zhang H, Xu M (2022) Multiscale emotion representation learning for affective image recognition. IEEE Trans Multimedia 25:2203\u20132212","journal-title":"IEEE Trans Multimedia"},{"issue":"10","key":"314_CR28","doi-asserted-by":"publisher","first-page":"6729","DOI":"10.1109\/TPAMI.2021.3094362","volume":"44","author":"S Zhao","year":"2021","unstructured":"Zhao S, Yao X, Yang J, Jia G, Ding G, Chua TS, Schuller BW, Keutzer K (2021) Affective image content analysis: two decades review and new perspectives. IEEE Trans Pattern Anal Mach Intell 44(10):6729\u20136751","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["International Journal of Multimedia Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13735-023-00314-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13735-023-00314-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13735-023-00314-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T11:12:27Z","timestamp":1711624347000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13735-023-00314-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,27]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["314"],"URL":"https:\/\/doi.org\/10.1007\/s13735-023-00314-4","relation":{},"ISSN":["2192-6611","2192-662X"],"issn-type":[{"value":"2192-6611","type":"print"},{"value":"2192-662X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,27]]},"assertion":[{"value":"10 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that the authors have no competing interests as defined by Springer.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"6"}}