{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:56:20Z","timestamp":1772906180445,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T00:00:00Z","timestamp":1677715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T00:00:00Z","timestamp":1677715200000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-023-01695-3","type":"journal-article","created":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T16:03:22Z","timestamp":1677773002000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Image-Based Sentiment Analysis Using InceptionV3 Transfer Learning Approach"],"prefix":"10.1007","volume":"4","author":[{"given":"Gaurav","family":"Meena","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7566-0703","authenticated-orcid":false,"given":"Krishna Kumar","family":"Mohbey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunil","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rahul Kumar","family":"Chawda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandeep V.","family":"Gaikwad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,2]]},"reference":[{"issue":"2","key":"1695_CR1","first-page":"1","volume":"3","author":"G Meena","year":"2022","unstructured":"Meena G, Mohbey KK, Indian A. Categorizing sentiment polarities in social networks data using convolutional neural network. SN Compt Sci. 2022;3(2):1\u20139.","journal-title":"SN Compt Sci"},{"key":"1695_CR2","volume-title":"Emotion detection and sentiment analysis of images","author":"V Gajarla","year":"2015","unstructured":"Gajarla V, Gupta A (2015) Emotion detection and sentiment analysis of images. Georgia Institute of Technology, Atlanta"},{"issue":"9","key":"1695_CR3","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1111\/mice.12363","volume":"33","author":"Y Gao","year":"2018","unstructured":"Gao Y, Mosalam KM. Deep transfer learning for image-based structural damage recognition. Compt-Aided Civil Infrastruct Eng. 2018;33(9):748\u201368.","journal-title":"Compt-Aided Civil Infrastruct Eng"},{"key":"1695_CR4","doi-asserted-by":"publisher","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826. https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"1695_CR5","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, S\u00e1nchez CI. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60\u201388.","journal-title":"Med Image Anal"},{"issue":"6","key":"1695_CR6","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1007\/s10796-021-10135-7","volume":"23","author":"H Kaur","year":"2021","unstructured":"Kaur H, Ahsaan SU, Alankar B, Chang V. A proposed sentiment analysis deep learning algorithm for analyzing COVID-19 tweets. Inf Syst Front. 2021;23(6):1417\u201329.","journal-title":"Inf Syst Front"},{"key":"1695_CR7","doi-asserted-by":"crossref","unstructured":"Poru\u0219niuc GC, Leon F, Timofte R, Miron C (2019) Convolutional neural networks architectures for facial expression recognition. In:\u00a02019 E-Health and Bioengineering Conference (EHB). IEEE. pp 1\u20136.","DOI":"10.1109\/EHB47216.2019.8969930"},{"key":"1695_CR8","doi-asserted-by":"publisher","first-page":"24321","DOI":"10.1109\/ACCESS.2019.2900231","volume":"7","author":"W Hua","year":"2019","unstructured":"Hua W, Dai F, Huang L, Xiong J, Gui G. HERO: Human emotions recognition for realizing intelligent Internet of Things. IEEE Access. 2019;7:24321\u201332.","journal-title":"IEEE Access"},{"key":"1695_CR9","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.ins.2020.04.041","volume":"533","author":"H Zheng","year":"2020","unstructured":"Zheng H, Wang R, Ji W, Zong M, Wong WK, Lai Z, Lv H. Discriminative deep multi-task learning for facial expression recognition. Inf Sci. 2020;533:60\u201371.","journal-title":"Inf Sci"},{"issue":"2","key":"1695_CR10","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s12530-021-09393-2","volume":"13","author":"A Boughida","year":"2022","unstructured":"Boughida A, Kouahla MN, Lafifi Y. A novel approach for facial expression recognition based on Gabor filters and genetic algorithm. Evol Syst. 2022;13(2):331\u201345.","journal-title":"Evol Syst"},{"issue":"2","key":"1695_CR11","first-page":"259","volume":"9","author":"MR Fallahzadeh","year":"2021","unstructured":"Fallahzadeh MR, Farokhi F, Harimi A, Sabbaghi-Nadooshan R. Facial expression recognition based on image gradient and deep convolutional neural network. J AI Data Min. 2021;9(2):259\u201368.","journal-title":"J AI Data Min"},{"issue":"4","key":"1695_CR12","first-page":"3578","volume":"18","author":"SB Mohammed","year":"2021","unstructured":"Mohammed SB, Abdulazeez AM. Deep convolution neural network for facial expression recognition. PalArch\u2019s J Archaeol Egypt\/Egyptol. 2021;18(4):3578\u201386.","journal-title":"PalArch\u2019s J Archaeol Egypt\/Egyptol"},{"key":"1695_CR13","doi-asserted-by":"publisher","first-page":"105724","DOI":"10.1016\/j.asoc.2019.105724","volume":"84","author":"JC Hung","year":"2019","unstructured":"Hung JC, Lin KC, Lai NX. Recognizing learning emotion based on convolutional neural networks and transfer learning. Appl Soft Comput. 2019;84:105724.","journal-title":"Appl Soft Comput"},{"issue":"3","key":"1695_CR14","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Fei-Fei L. Imagenet large scale visual recognition challenge. Intern J Compt Vis. 2015;115(3):211\u201352.","journal-title":"Intern J Compt Vis."},{"issue":"6","key":"1695_CR15","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"issue":"3","key":"1695_CR16","doi-asserted-by":"publisher","first-page":"242","DOI":"10.3311\/PPtr.11480","volume":"47","author":"C Lin","year":"2019","unstructured":"Lin C, Li L, Luo W, Wang KC, Guo J. Transfer learning based traffic sign recognition using inception-v3 model. Period Polytech Transp Eng. 2019;47(3):242\u201350.","journal-title":"Period Polytech Transp Eng"},{"key":"1695_CR17","doi-asserted-by":"crossref","unstructured":"Raina R, Battle A, Lee H, Packer B, Ng AY (2007) Self-taught learning: transfer learning from unlabeled data. In\u00a0Proceedings of the 24th international conference on Machine learning.\u00a0(pp. 759\u2013766).","DOI":"10.1145\/1273496.1273592"},{"key":"1695_CR18","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/978-3-642-42051-1_16","volume-title":"International conference on neural information processing","author":"IJ Goodfellow","year":"2013","unstructured":"Goodfellow IJ, Erhan D, Carrier PL, Courville A, Mirza M, Hamner B, Bengio Y. Challenges in representation learning: a report on three machine learning contests. In: International conference on neural information processing. Berlin Heidelberg: Springer; 2013. p. 117\u201324."},{"issue":"9","key":"1695_CR19","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.3390\/electronics10091036","volume":"10","author":"MAH Akhand","year":"2021","unstructured":"Akhand MAH, Roy S, Siddique N, Kamal MAS, Shimamura T. Facial emotion recognition using transfer learning in the deep CNN. Electronics. 2021;10(9):1036.","journal-title":"Electronics"},{"key":"1695_CR20","doi-asserted-by":"crossref","unstructured":"Lucey P, Cohn JF, Kanade T, Saragih J, Ambadar Z, Matthews I (2010) The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In:\u00a02010 ieee computer society conference on computer vision and pattern recognition-workshops.\u00a0(pp. 94\u2013101). IEEE.","DOI":"10.1109\/CVPRW.2010.5543262"},{"issue":"1","key":"1695_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42488-019-00013-y","volume":"2","author":"KK Mohbey","year":"2020","unstructured":"Mohbey KK. Multi-class approach for user behavior prediction using deep learning framework on twitter election dataset. J Data Inform Manage. 2020;2(1):1\u201314.","journal-title":"J Data Inform Manage"},{"key":"1695_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-021-03488-z","author":"M Bansal","year":"2021","unstructured":"Bansal M, Kumar M, Sachdeva M, Mittal A. Transfer learning for image classification using VGG19: caltech-101 image data set. J Am Intell Human Compt. 2021. https:\/\/doi.org\/10.1007\/s12652-021-03488-z.","journal-title":"J Am Intell Human Compt"},{"key":"1695_CR23","unstructured":"Albu F, Hagiescu D, Vladutu L, Puica MA (2015) Neural network approaches for children\u2019s emotion recognition in intelligent learning applications. In:\u00a0EDULEARN15 7th Annu Int Conf Educ New Learn Technol. 6th-8th. Barcelona. Spain."},{"key":"1695_CR24","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition.\u00a0arXiv preprint arXiv:1409.1556."},{"issue":"16","key":"1695_CR25","doi-asserted-by":"publisher","first-page":"5361","DOI":"10.3390\/s21165361","volume":"21","author":"S Ahmed","year":"2021","unstructured":"Ahmed S, Shaikh A, Alshahrani H, Alghamdi A, Alrizq M, Baber J, Bakhtyar M. Transfer learning approach for classification of histopathology whole slide images. Sensors. 2021;21(16):5361.","journal-title":"Sensors"},{"key":"1695_CR26","doi-asserted-by":"crossref","unstructured":"Shaees S, Naeem H, Arslan M, Naeem MR, Ali SH, Aldabbas H (2020) Facial emotion recognition using transfer learning. In:\u00a02020 International Conference on Computing and Information Technology (ICCIT-1441).\u00a0(pp. 1\u20135). IEEE.","DOI":"10.1109\/ICCIT-144147971.2020.9213757"},{"key":"1695_CR27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11231","author":"C Szegedy","year":"2017","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA. Inception-v4, inception-resnet and the impact of residual connections on learning. Thirty-first AAAI Conf Artificial Intell. 2017. https:\/\/doi.org\/10.1609\/aaai.v31i1.11231.","journal-title":"Thirty-first AAAI Conf Artificial Intell"},{"issue":"1","key":"1695_CR28","doi-asserted-by":"publisher","first-page":"012015","DOI":"10.1088\/1742-6596\/2025\/1\/012015","volume":"2025","author":"L Yang","year":"2021","unstructured":"Yang L, Zhang H, Li D, Xiao F, Yang S. Facial expression recognition based on transfer learning and SVM. J Phys Conf Ser. 2021;2025(1):012015.","journal-title":"J Phys Conf Ser."},{"issue":"1","key":"1695_CR29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42979-021-00920-1","volume":"3","author":"NV Babu","year":"2022","unstructured":"Babu NV, Kanaga E. Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN Compt Sci. 2022;3(1):1\u201320.","journal-title":"SN Compt Sci"},{"issue":"1","key":"1695_CR30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42979-021-00920-1","volume":"3","author":"S Chakraborty","year":"2022","unstructured":"Chakraborty S, Paul S, Hasan KM. A transfer learning-based approach with deep cnn for covid-19-and pneumonia-affected chest x-ray image classification. SN Compt Sci. 2022;3(1):1\u201310.","journal-title":"SN Compt Sci"},{"key":"1695_CR31","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.procs.2022.08.050","volume":"204C","author":"G Meena","year":"2022","unstructured":"Meena G, Mohbey KK, Kumar S, Indian A. Sentiment analysis from images using vgg19 based transfer learning approach. Proc Compt Sci. 2022;204C:411\u20138.","journal-title":"Proc Compt Sci"}],"updated-by":[{"DOI":"10.1007\/s42979-023-01874-2","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000}}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-01695-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-023-01695-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-01695-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T12:44:54Z","timestamp":1684413894000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-023-01695-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,2]]},"references-count":31,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["1695"],"URL":"https:\/\/doi.org\/10.1007\/s42979-023-01695-3","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s42979-023-01874-2","asserted-by":"object"}]},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,2]]},"assertion":[{"value":"10 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2023","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s42979-023-01874-2","URL":"https:\/\/doi.org\/10.1007\/s42979-023-01874-2","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors of this manuscript state that they have no conflict of interest.","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":"242"}}