{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T19:57:02Z","timestamp":1781035022211,"version":"3.54.1"},"reference-count":115,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T00:00:00Z","timestamp":1780963200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T00:00:00Z","timestamp":1780963200000},"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":["Knowl Inf Syst"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s10115-026-02787-1","type":"journal-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T17:49:12Z","timestamp":1781027352000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Decoding emotions: a survey of multimodal sentiment analysis in the digital age in artificial intelligence"],"prefix":"10.1007","volume":"68","author":[{"given":"Akanksha","family":"Puri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. L.","family":"Sangal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harsh K.","family":"Verma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shilpa","family":"Mahajan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,9]]},"reference":[{"key":"2787_CR1","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1109\/TAFFC.2020.3038167","volume":"14","author":"S Poria","year":"2020","unstructured":"Poria S, Hazarika D, Majumder N, Mihalcea R (2020) Beneath the tip of the iceberg: current challenges and new directions in sentiment analysis research. IEEE Trans Affect Comput 14:108","journal-title":"IEEE Trans Affect Comput"},{"key":"2787_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107134","volume":"226","author":"M Birjali","year":"2021","unstructured":"Birjali M, Kasri M, Beni-Hssane A (2021) A comprehensive survey on sentiment analysis: approaches, challenges and trends. Knowl-Based Syst 226:107134","journal-title":"Knowl-Based Syst"},{"key":"2787_CR3","doi-asserted-by":"crossref","unstructured":"Faturohman F, Irawan B, Setianingsih C (2021) Sentiment analysis on social security administrator for health using recurrent neural network. Institute of Electrical and Electronics Engineers (IEEE)","DOI":"10.1109\/ISRITI54043.2021.9702816"},{"key":"2787_CR4","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.inffus.2017.12.006","volume":"44","author":"I Chaturvedi","year":"2017","unstructured":"Chaturvedi I, Cambria E, Welsch RE, Herrera F (2017) Distinguishing between facts and opinions for sentiment analysis: survey and challenges. Inf Fusion 44:65\u201377","journal-title":"Inf Fusion"},{"issue":"10","key":"2787_CR5","first-page":"7349","volume":"34","author":"S Deng","year":"2024","unstructured":"Deng S, Wu L, Shi G et al (2024) One for all: a unified generative framework for image emotion classification. IEEE Trans Circuits Syst Video Technol 34(10):7349\u20137360","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"2787_CR6","unstructured":"Sarker IH, Khan AI, Binti Farhana J (2020) Sentiment analysis on mental health issues in social media. Appl Data Sci Mach Learn 2020"},{"issue":"11","key":"2787_CR7","first-page":"37","volume":"111","author":"K Ravi","year":"2015","unstructured":"Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis. Int J Comput Appl 111(11):37\u201346","journal-title":"Int J Comput Appl"},{"issue":"2","key":"2787_CR8","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1162\/COLI_a_00049","volume":"37","author":"M Taboada","year":"2011","unstructured":"Taboada M, Brooke J, Tofiloski M, Buie E, Voll K (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267\u2013307","journal-title":"Comput Linguist"},{"key":"2787_CR9","unstructured":"Devlin J, Chang M W, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)"},{"key":"2787_CR10","doi-asserted-by":"crossref","unstructured":"Turney PD (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on Association for Computational Linguistics (ACL)","DOI":"10.3115\/1073083.1073153"},{"issue":"4","key":"2787_CR11","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/s12559-016-9415-7","volume":"8","author":"K Dashtipour","year":"2016","unstructured":"Dashtipour K, Poria S, Hussain A, Cambria E, Hawalah AYA, Gelbukh A et al (2016) Multilingual sentiment analysis: state of the art and independent comparison of techniques. Cogn Comput 8(4):757\u2013771","journal-title":"Cogn Comput"},{"key":"2787_CR12","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1757\/1\/012128","author":"Y Liu","year":"2021","unstructured":"Liu Y, Liu B, Yu J, Yu Z (2021) Multi-angle movie reviews analysis based on multi model. J Phys: Conf Ser. https:\/\/doi.org\/10.1088\/1742-6596\/1757\/1\/012128","journal-title":"J Phys: Conf Ser"},{"issue":"7","key":"2787_CR13","doi-asserted-by":"publisher","first-page":"337","DOI":"10.14569\/IJACSA.2021.0120738","volume":"12","author":"Z Madhoushi","year":"2021","unstructured":"Madhoushi Z, Hamdan AR, Zainudin S (2021) A similarity score model for aspect category detection. Int J Adv Comput Sci Appl 12(7):337\u2013344. https:\/\/doi.org\/10.14569\/IJACSA.2021.0120738","journal-title":"Int J Adv Comput Sci Appl"},{"key":"2787_CR14","doi-asserted-by":"crossref","unstructured":"Rawat R, Mahor V, Chirgaiya S, Shaw RN, Ghosh A(2021) Sentiment analysis at online social network for cyber-malicious post reviews using machine learning techniques. Springer Singapore, Singapore. pp. 113\u2013130","DOI":"10.1007\/978-981-16-0407-2_9"},{"key":"2787_CR15","doi-asserted-by":"publisher","unstructured":"Amin MS, Ahn H, Choi YB (2021) Human sentiments and associated physical actions detection in disasters with deep learning. In: 3rd international conference on artificial intelligence in information and communication, ICAIIC 2021:111-113 https:\/\/doi.org\/10.1109\/ICAIIC51459.2021.9415229","DOI":"10.1109\/ICAIIC51459.2021.9415229"},{"key":"2787_CR16","doi-asserted-by":"crossref","unstructured":"Zadeh A, Zellers R (2016) Multimodal sentiment intensity analysis in videos : facial gestures and verbal messages","DOI":"10.1109\/MIS.2016.94"},{"issue":"2","key":"2787_CR17","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/MIS.2013.30","volume":"28","author":"E Cambria","year":"2013","unstructured":"Cambria E, Schuller B, Xia Y, Havasi C (2013) New avenues in opinion mining and sentiment analysis. IEEE Intell Syst 28(2):15\u201321","journal-title":"IEEE Intell Syst"},{"key":"2787_CR18","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.eswa.2018.10.003","volume":"118","author":"HH Do","year":"2019","unstructured":"Do HH, Prasad PW, Maag A, Alsadoon A (2019) Deep learning for aspect-based sentiment analysis: a comparative review. Expert Syst Appl 118:272\u2013299","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2787_CR19","doi-asserted-by":"publisher","first-page":"445","DOI":"10.47836\/pjst.29.1.25","volume":"29","author":"NAS Abdullah","year":"2021","unstructured":"Abdullah NAS, Rusli NIA (2021) Multilingual sentiment analysis: a systematic literature review. Pertan J Sci Technol 29(1):445\u2013470. https:\/\/doi.org\/10.47836\/pjst.29.1.25","journal-title":"Pertan J Sci Technol"},{"key":"2787_CR20","unstructured":"http:\/\/www.kyb.tuebingen.mpg.de\/fileadmin\/user_upload\/files\/publications\/ssl-book_3888%5B0%5D.pdf"},{"key":"2787_CR21","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.neucom.2021.02.020","volume":"457","author":"K Dashtipour","year":"2021","unstructured":"Dashtipour K, Gogate M, Cambria E, Hussain A (2021) A novel context-aware multimodal framework for Persian sentiment analysis. Neurocomputing 457:377\u2013388","journal-title":"Neurocomputing"},{"issue":"40","key":"2787_CR22","first-page":"327","volume":"9","author":"A Puri","year":"2016","unstructured":"Puri A, Kaur S, Mohana R (2016) Temporal sentiment analysis: a review. Int J Control Theory Appl 9(40):327\u2013334","journal-title":"Int J Control Theory Appl"},{"key":"2787_CR23","doi-asserted-by":"crossref","unstructured":"Taneja N, Thakur HK (2021) RNNCore: Lexicon aided recurrent neural network for sentiment analysis. Int J Comput Digit Syst","DOI":"10.12785\/ijcds\/1201126"},{"key":"2787_CR24","volume":"243","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Li X, Liu Z (2022) Multimodal interaction enhanced representation learning for video emotion recognition. Knowl-Based Syst 243:108456","journal-title":"Knowl-Based Syst"},{"key":"2787_CR25","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","volume":"109","author":"JE Van Engelen","year":"2020","unstructured":"Van Engelen JE, Hoos HH (2020) A survey on semi-supervised learning. Mach Learn 109:373\u2013440","journal-title":"Mach Learn"},{"key":"2787_CR26","doi-asserted-by":"crossref","unstructured":"LeCun Y, Bengio Y (2015) Geoffrey HintonTitleDeep LearningJournalNatureVolume\/Issue\/PageVol. 521, No. 7553, pp. 436-444 Publication DateMay 28","DOI":"10.1038\/nature14539"},{"key":"2787_CR27","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press"},{"key":"2787_CR28","doi-asserted-by":"crossref","unstructured":"Raisa JF, Ulfat M, Al Mueed A, Reza SM(2021) A review on Twitter sentiment analysis approaches. In: 2021 international conference on information and communication technology for sustainable development, ICICT4SD 2021 - Proceedings. pp. 375-379","DOI":"10.1109\/ICICT4SD50815.2021.9396915"},{"issue":"2","key":"2787_CR29","doi-asserted-by":"publisher","first-page":"154","DOI":"10.25103\/jestr.132.19","volume":"13","author":"S Sagnika","year":"2020","unstructured":"Sagnika S, Pattanaik A, Mishra BSP, Meher SK (2020) A review on multi-lingual sentiment analysis by machine learning methods. J Eng Sci Technol Rev 13(2):154\u2013166. https:\/\/doi.org\/10.25103\/jestr.132.19","journal-title":"J Eng Sci Technol Rev"},{"key":"2787_CR30","unstructured":"Jigneshkumar Patel H, Prakash Verma J, Patel A (2021) In evolving technologies for computing, communication and smart world. In: Lecture Notes in Electrical Engineering, vol. 694, Springer"},{"key":"2787_CR31","doi-asserted-by":"crossref","unstructured":"Xu Y, Cao H, Du W, Wang W (2022) A survey of cross-lingual sentiment analysis: methodologies, models and evaluations. Data Sci Eng (0123456789)","DOI":"10.1007\/s41019-022-00187-3"},{"key":"2787_CR32","doi-asserted-by":"crossref","unstructured":"Hu G, Lin TE, Zhao Y, Lu G, Wu Y, Li Y (2022) UniMSE: Towards unified multimodal sentiment analysis and emotion recognition","DOI":"10.18653\/v1\/2022.emnlp-main.534"},{"issue":"3","key":"2787_CR33","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1007\/s11063-021-10713-5","volume":"54","author":"X Zhuang","year":"2022","unstructured":"Zhuang X, Liu F, Hou J, Hao J, Cai X (2022) Transformer-based interactive multimodal attention network for video sentiment detection. Neural Process Lett 54(3):1943\u20131960","journal-title":"Neural Process Lett"},{"key":"2787_CR34","doi-asserted-by":"crossref","unstructured":"Aicher A, Vinogradova A, Gusev A, Matveev Y, Minker W (2022) Towards speech-only opinion-level sentiment analysis. 2000\u20132006","DOI":"10.63317\/3xp3jd9zfmx3"},{"key":"2787_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109259","volume":"136","author":"D Wang","year":"2023","unstructured":"Wang D, Guo X, Tian Y, Liu J, He LH, Luo X (2023) TETFN: a text enhanced transformer fusion network for multimodal sentiment analysis. Pattern Recogn 136:109259","journal-title":"Pattern Recogn"},{"issue":"1","key":"2787_CR36","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/s11277-021-08474-4","volume":"120","author":"MS Hossen","year":"2021","unstructured":"Hossen MS, Dev NR (2021) An improved Lexicon based model for efficient sentiment analysis on movie review data. Wirel Pers Commun 120(1):535\u2013544","journal-title":"Wirel Pers Commun"},{"key":"2787_CR37","unstructured":"Elgabry H, Attia S, Abdel-Rahman A, Abdel-Ate A, Girgis S(2021) A contextual word embedding for Arabic sarcasm detection with random forests. In: Proceedings of the sixth Arabic natural language processing workshop. pp. 340\u2013344"},{"key":"2787_CR38","doi-asserted-by":"publisher","unstructured":"Saura JR, Ribeiro-Soriano D, Palacios-Marque\u2019s D(2021) Evaluating security and privacy issues of social networks based information systems in Industry 4.0. Enterprise Information Systems https:\/\/doi.org\/10.1080\/17517575.2021.1913765","DOI":"10.1080\/17517575.2021.1913765"},{"issue":"1","key":"2787_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-021-00776-6","volume":"11","author":"P Nandwani","year":"2021","unstructured":"Nandwani P, Verma R (2021) A review on sentiment analysis and emotion detection from text. Soc Netw Anal Min 11(1):1\u201319","journal-title":"Soc Netw Anal Min"},{"key":"2787_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100413","volume":"41","author":"PK Jain","year":"2021","unstructured":"Jain PK, Pamula R, Srivastava G (2021) A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews. Comput Sci Rev 41:100413","journal-title":"Comput Sci Rev"},{"key":"2787_CR41","unstructured":"Liu J, Luo X, Lin P, Fan Y (2022) Fine-grained sentiment analysis recent progress 16(2):21\u201330"},{"key":"2787_CR42","doi-asserted-by":"crossref","unstructured":"Zad S, Heidari M, Jones JH ,Uzuner O (2021) A survey on concept-level sentiment analysis techniques of textual data. In: 2021 IEEE world AI IoT congress, AIIoT","DOI":"10.1109\/AIIoT52608.2021.9454169"},{"issue":"2","key":"2787_CR43","first-page":"601","volume":"9","author":"P Mehta","year":"2020","unstructured":"Mehta P, Pandya S (2020) A review on sentiment analysis methodologies, practices and applications. Int J Sci Technol Res 9(2):601\u2013609","journal-title":"Int J Sci Technol Res"},{"issue":"1","key":"2787_CR44","doi-asserted-by":"publisher","first-page":"77","DOI":"10.2174\/2666255813999200918123059","volume":"15","author":"MV Naik","year":"2020","unstructured":"Naik MV, Kumar APS (2020) A novel approach for extraction of distinguishing emotions for semantic granularity level sentiment analysis in multilingual context. Recent Adv Comput Sci Commun 15(1):77\u201387","journal-title":"Recent Adv Comput Sci Commun"},{"issue":"4","key":"2787_CR45","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/j.jksues.2016.04.002","volume":"30","author":"DMEDM Hussein","year":"2018","unstructured":"Hussein DMEDM (2018) A survey on sentiment analysis challenges. J King Saud Univ Eng Sci 30(4):330\u2013338","journal-title":"J King Saud Univ Eng Sci"},{"key":"2787_CR46","first-page":"1541","volume":"13","author":"M W\u00f6llmer","year":"2013","unstructured":"W\u00f6llmer M, Weninger F, Knaup T, Schuller B, Sun C, Sagae K et al (2013) IEEE intelligent systems YouTube movie reviews: sentiment analysis in an audio- visual context. IEEE Comput Soc 13:1541\u20131672","journal-title":"IEEE Comput Soc"},{"key":"2787_CR47","doi-asserted-by":"crossref","unstructured":"Vanaja S (2018) Aspect-level sentiment analysis on E-commerce data (ICIRCA). pp. 1275-1279","DOI":"10.1109\/ICIRCA.2018.8597286"},{"key":"2787_CR48","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.aci.2019.02.002","volume":"18","author":"P Ray","year":"2020","unstructured":"Ray P, Chakrabarti A (2020) A mixed approach of deep learning method and rule-based method to improve aspect level sentiment analysis. Appl Comput Inform 18:163","journal-title":"Appl Comput Inform"},{"issue":"1","key":"2787_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","volume":"2","author":"J Bollen","year":"2011","unstructured":"Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1\u20138","journal-title":"J Comput Sci"},{"issue":"2","key":"2787_CR50","doi-asserted-by":"publisher","first-page":"106","DOI":"10.14569\/IJACSA.2018.090216","volume":"9","author":"S Al-Otaibi","year":"2018","unstructured":"Al-Otaibi S, Alnassar A, Alshahrani A, Al-Mubarak A, Albugami S, Almutiri N et al (2018) Customer satisfaction measurement using sentiment analysis. Int J Adv Comput Sci Appl 9(2):106\u2013117. https:\/\/doi.org\/10.14569\/IJACSA.2018.090216","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"10","key":"2787_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2019.e02690","volume":"5","author":"P Rita","year":"2019","unstructured":"Rita P, Oliveira T, Farisa A (2019) The impact of e-service quality and customer satisfaction on customer behavior in online shopping. Heliyon 5(10):e02690","journal-title":"Heliyon"},{"issue":"15","key":"2787_CR52","first-page":"69","volume":"8","author":"SF Sayeedunnisa","year":"2020","unstructured":"Sayeedunnisa SF (2020) Using slang and emoticon for sentiment analysis of social media data. Int J Eng Res Technol (IJERT) 8(15):69\u201372","journal-title":"Int J Eng Res Technol (IJERT)"},{"issue":"10","key":"2787_CR53","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/978-3-030-86165-0_21","volume":"12","author":"P Suresh","year":"2022","unstructured":"Suresh P, Gurumoorthy K (2022) Mining of customer review feedback using sentiment analysis for smart phone product. EAI\/Springer Innov Commun Comput 12(10):247\u2013259. https:\/\/doi.org\/10.1007\/978-3-030-86165-0_21","journal-title":"EAI\/Springer Innov Commun Comput"},{"key":"2787_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2022.101588","volume":"52","author":"Z Wang","year":"2022","unstructured":"Wang Z, Gao P, Chu X (2022) Sentiment analysis from customer-generated online videos on product review using topic modeling and multi-attention BLSTM. Adv Eng Inform 52:101588","journal-title":"Adv Eng Inform"},{"key":"2787_CR55","unstructured":"Musa YY, Wang JE (2013) Int J Eng Res Technol 2(2):1\u20138"},{"issue":"16","key":"2787_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/electronics12163516","volume":"12","author":"L Deng","year":"2023","unstructured":"Deng L, Liu B, Li Z, Ma J, Li H (2023) Context-dependent multimodal sentiment analysis based on a complex attention mechanism. Electronics (Switzerland) 12(16):1\u201313. https:\/\/doi.org\/10.3390\/electronics12163516","journal-title":"Electronics (Switzerland)"},{"issue":"1","key":"2787_CR57","doi-asserted-by":"publisher","first-page":"13491","DOI":"10.1038\/s41598-024-62990-4","volume":"14","author":"X Zhang","year":"2024","unstructured":"Zhang X, Cheng X, Liu H (2024) TPRO-NET: an EEG-based emotion recognition method reflecting subtle changes in emotion. Sci Rep 14(1):13491","journal-title":"Sci Rep"},{"key":"2787_CR58","doi-asserted-by":"crossref","unstructured":"Deng DQ, Chen X (2025) Intervening in negative emotion contagion on social networks using reinforcement learning. In: IEEE transactions on computational social systems, pp. 1-12","DOI":"10.1109\/TCSS.2025.3555607"},{"key":"2787_CR59","doi-asserted-by":"crossref","unstructured":"Xu H (2023b) Multimodal sentiment analysis. Springer Nature Singapore, Singapore, pp 217\u2013240","DOI":"10.1007\/978-981-99-5776-7_6"},{"issue":"6","key":"2787_CR60","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1007\/s41095-023-0389-6","volume":"10","author":"S Deng","year":"2022","unstructured":"Deng S, Wu L, Shi G, Xing L, Jian M, Xiang Y, Dong R (2022) Learning to compose diversified prompts for image emotion classification. Comput Vis Media 10(6):1169\u201383","journal-title":"Comput Vis Media"},{"issue":"21","key":"2787_CR61","doi-asserted-by":"publisher","first-page":"23745","DOI":"10.1007\/s11042-024-19999-8","volume":"84","author":"G Ahuja","year":"2025","unstructured":"Ahuja G, Alaei A, Pal U (2025) A new multimodal sentiment analysis for images containing textual information. Multimed Tools Appl 84(21):23745\u201323774","journal-title":"Multimed Tools Appl"},{"key":"2787_CR62","doi-asserted-by":"publisher","unstructured":"Morency LP, Mihalcea R, Doshi P (2011) Towards multimodal sentiment analysis. pp. 169\u2013176 https:\/\/doi.org\/10.1145\/2070481.2070509","DOI":"10.1145\/2070481.2070509"},{"key":"2787_CR63","unstructured":"Yang H, Zhao Y, Liu J, Wu Y, Qin B (2022) MACSA: a multimodal aspect-category sentiment analysis dataset with multimodal fine-grained aligned annotations; vol. 1. Association for Computing Machinery"},{"key":"2787_CR64","doi-asserted-by":"crossref","unstructured":"Boukabous M, Azizi M (2022) Multimodal sentiment analysis using audio and text for crime detection. In: 2022 2nd international conference on innovative research in applied science, engineering and technology, IRASET","DOI":"10.1109\/IRASET52964.2022.9738175"},{"key":"2787_CR65","unstructured":"Sasi Kiran Reddy K, Harshith SVSC (2021) SSRBP. IRJET- sentiment analysis of polarity in product reviews in Amazon Product Media using multi model classification. IRJET 8(5):-8"},{"key":"2787_CR66","unstructured":"Mihalcea R, Morency L, Science C (2013) Utterance-level multimodal sentiment analysis P13-1096.pdf. Acl 973-982"},{"key":"2787_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117575","volume":"204","author":"R Das","year":"2022","unstructured":"Das R, Singh TD (2022) A multi-stage multimodal framework for sentiment analysis of Assamese in low resource setting. Expert Syst Appl 204:117575","journal-title":"Expert Syst Appl"},{"key":"2787_CR68","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.knosys.2019.01.019","volume":"167","author":"F Huang","year":"2019","unstructured":"Huang F, Zhang X, Zhao Z, Xu J, Li Z (2019) Image-text sentiment analysis via deep multimodal attentive fusion. Knowl-Based Syst 167:26\u201337","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"2787_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103193","volume":"60","author":"D Chen","year":"2023","unstructured":"Chen D, Su W, Wu P, Hua B (2023) Joint multimodal sentiment analysis based on information relevance. Inf Process Manag 60(2):103193. https:\/\/doi.org\/10.1016\/j.ipm.2022.103193","journal-title":"Inf Process Manag"},{"key":"2787_CR70","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.126974","volume":"276","author":"X Zhao","year":"2025","unstructured":"Zhao X, Poria S, Li X, Chen Y, Tang B (2025) Toward robust multimodal sentiment analysis using multimodal foundational models. Expert Syst Appl 276:126974","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2787_CR71","doi-asserted-by":"publisher","first-page":"2126","DOI":"10.1038\/s41598-025-85859-6","volume":"15","author":"Y Cai","year":"2025","unstructured":"Cai Y, Li X, Zhang Y, Li J, Zhu F, Rao L (2025) Multimodal sentiment analysis based on multi-layer feature fusion and multi-task learning. Sci Rep 15(1):2126","journal-title":"Sci Rep"},{"issue":"2","key":"2787_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-023-10645-7","volume":"57","author":"B Subbaiah","year":"2024","unstructured":"Subbaiah B, Murugesan K, Saravanan P, Marudhamuthu K (2024) An efficient multimodal sentiment analysis in social media using hybrid optimal multi-scale residual attention network. Artif Intell Rev 57(2):1\u201327","journal-title":"Artif Intell Rev"},{"key":"2787_CR73","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107676","volume":"235","author":"T Wu","year":"2022","unstructured":"Wu T, Peng J, Zhang W, Zhang H, Tan S, Yi F et al (2022) Video sentiment analysis with bimodal information- augmented multi-head attention. Knowl-Based Syst 235:107676","journal-title":"Knowl-Based Syst"},{"key":"2787_CR74","doi-asserted-by":"publisher","unstructured":"Zadeh A, Liang PP, Vanbriesen J, Poria S, Tong E, Cambria E et\u00a0al (2018) Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph. In: ACL 2018 - 56th annual meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) 1:2236-2246. https:\/\/doi.org\/10.18653\/v1\/p18-1208","DOI":"10.18653\/v1\/p18-1208"},{"issue":"4","key":"2787_CR75","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s10579-008-9076-6","volume":"42","author":"C Busso","year":"2008","unstructured":"Busso C, Bulut M, Lee CC, Kazemzadeh A, Mower E, Kim S et al (2008) IEMOCAP: interactive emotional dyadic motion capture database. Lang Resour Eval 42(4):335\u2013359. https:\/\/doi.org\/10.1007\/s10579-008-9076-6","journal-title":"Lang Resour Eval"},{"issue":"3","key":"2787_CR76","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102929","volume":"59","author":"A Ghorbanali","year":"2022","unstructured":"Ghorbanali A, Sohrabi MK, Yaghmaee F (2022) Ensemble transfer learning-based multimodal sentiment analysis using weighted convolutional neural networks. Inf Process Manag 59(3):102929","journal-title":"Inf Process Manag"},{"issue":"6","key":"2787_CR77","doi-asserted-by":"publisher","first-page":"856","DOI":"10.14569\/IJACSA.2024.0150686","volume":"15","author":"MB Habib","year":"2024","unstructured":"Habib MB, Hafiz MFB, Khan NA, Hossain S (2024) Multimodal sentiment analysis using deep learning fusion techniques and transformers. Int J Adv Comput Sci Appl 15(6):856\u2013863. https:\/\/doi.org\/10.14569\/IJACSA.2024.0150686","journal-title":"Int J Adv Comput Sci Appl"},{"key":"2787_CR78","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1007\/s41060-023-00497-3","volume":"20","author":"S Wang","year":"2024","unstructured":"Wang S, Cai G, Lv G (2024) Aspect-level multimodal sentiment analysis based on co-attention fusion. Int J Data Sci Anal 20:903","journal-title":"Int J Data Sci Anal"},{"key":"2787_CR79","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111965","volume":"297","author":"Y Fu","year":"2024","unstructured":"Fu Y, Huang B, Wen Y, Zhang P (2024) FDR-MSA: enhancing multimodal sentiment analysis through feature disentanglement and reconstruction. Knowl-Based Syst 297:111965","journal-title":"Knowl-Based Syst"},{"key":"2787_CR80","doi-asserted-by":"crossref","unstructured":"Li T, Chen X, Dong Z, Keutzer K, Zhang S(2022) Domain-adaptive text classification with structured knowledge from unlabeled data (Figure 1):4216-4222","DOI":"10.24963\/ijcai.2022\/585"},{"issue":"10","key":"2787_CR81","doi-asserted-by":"publisher","first-page":"11184","DOI":"10.1007\/s10489-021-02936-9","volume":"52","author":"W Liao","year":"2022","unstructured":"Liao W, Zeng B, Liu J, Wei P, Fang J (2022) Image-text interaction graph neural network for image-text sentiment analysis. Appl Intell 52(10):11184\u201311198. https:\/\/doi.org\/10.1007\/s10489-021-02936-9","journal-title":"Appl Intell"},{"key":"2787_CR82","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.108107","volume":"240","author":"Y Du","year":"2022","unstructured":"Du Y, Liu Y, Peng Z, Jin X (2022) Gated attention fusion network for multimodal sentiment classification. Knowl-Based Syst 240:108107","journal-title":"Knowl-Based Syst"},{"key":"2787_CR83","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.neucom.2021.06.026","volume":"457","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Wang S, Liu Z, Chen X (2021) TIMAN: transformer-based interactive multimodal attention network for sentiment analysis and emotion recognition. Neurocomputing 457:564\u2013572","journal-title":"Neurocomputing"},{"issue":"3","key":"2787_CR84","doi-asserted-by":"publisher","first-page":"1082","DOI":"10.1007\/s12559-023-10119-6","volume":"15","author":"D Jiang","year":"2023","unstructured":"Jiang D, Liu H, Wei R, Tu G (2023) CSAT-FTCN: a fuzzy-oriented model with contextual self-attention network for multimodal emotion recognition. Cogn Comput 15(3):1082\u20131091","journal-title":"Cogn Comput"},{"key":"2787_CR85","doi-asserted-by":"crossref","unstructured":"Chauhan DS, et\u00a0al (2019) Context-aware interactive attention for multimodal sentiment and emotion analysis. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP)","DOI":"10.18653\/v1\/D19-1566"},{"issue":"5","key":"2787_CR86","volume":"59","author":"G Tu","year":"2022","unstructured":"Tu G, Wang J, Zhang Y, Liu Y (2022) Sentic GAT: a graph attention network for sentiment-aware emotion recognition in conversations. Inf Process Manag 59(5):103050","journal-title":"Inf Process Manag"},{"key":"2787_CR87","first-page":"1234","volume":"218","author":"A Juyal","year":"2022","unstructured":"Juyal A, Sharma V, Mamgain N (2022) Multimodal emotion recognition using audio-visual features for embodied conversational agents. Proc Comput Sci 218:1234\u20131243","journal-title":"Proc Comput Sci"},{"issue":"6","key":"2787_CR88","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103508","volume":"60","author":"L Xiao","year":"2023","unstructured":"Xiao L, Wu X, Yang S, Xu J, Zhou J, He L (2023) Cross-modal fine-grained alignment and fusion network for multimodal aspect-based sentiment analysis. Inf Process Manag 60(6):103508","journal-title":"Inf Process Manag"},{"key":"2787_CR89","doi-asserted-by":"publisher","first-page":"45678","DOI":"10.1109\/ACCESS.2023.3244390","volume":"11","author":"T Le","year":"2023","unstructured":"Le T, Nguyen H, Tran M, Nguyen A (2023) Multimodal emotion recognition using transformer-based fusion and representation learning. IEEE Access 11:45678\u201345690","journal-title":"IEEE Access"},{"key":"2787_CR90","doi-asserted-by":"crossref","unstructured":"Zhu Z, Yang H, Tang M, Yang Z, Eskimez SE, Wang H (2023) Real-time audio-visual end-to-end speech enhancement","DOI":"10.1109\/ICASSP49357.2023.10094724"},{"key":"2787_CR91","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109731","volume":"140","author":"H Wang","year":"2025","unstructured":"Wang H, Ren C, Yu Z (2025) Multimodal sentiment analysis based on multiple attention. Eng Appl Artif Intell 140:109731","journal-title":"Eng Appl Artif Intell"},{"key":"2787_CR92","doi-asserted-by":"crossref","unstructured":"Wang P, Zhou Q, Wu Y, Chen T, Hu J (2025) DLF: Disentangled-language-focused multimodal sentiment analysis. In: Proceedings of AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v39i20.35416"},{"key":"2787_CR93","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2023.102563","volume":"80","author":"S Lai","year":"2023","unstructured":"Lai S, Hu X, Xu H, Ren Z, Liu Z (2023) Multimodal sentiment analysis: a survey. Displays 80:102563. https:\/\/doi.org\/10.1016\/j.displa.2023.102563","journal-title":"Displays"},{"key":"2787_CR94","doi-asserted-by":"crossref","unstructured":"Mao H, Zhang B, Xu H, Yuan Z, Liu Y(2023 ) Robust-MSA: Understanding the impact of modality noise on multimodal sentiment analysis. In: Proceedings of the 37th AAAI conference on artificial intelligence, AAAI 2023;37:16458-16460","DOI":"10.1609\/aaai.v37i13.27078"},{"issue":"1","key":"2787_CR95","first-page":"876","volume":"7","author":"MG Huddar","year":"2019","unstructured":"Huddar MG, Sannakki SS, Rajpurohit VS (2019) A survey of computational approaches and challenges in multimodal sentiment analysis. Int J Comput Sci Eng 7(1):876\u2013883","journal-title":"Int J Comput Sci Eng"},{"issue":"1","key":"2787_CR96","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-024-60210-7","volume":"14","author":"MSU Miah","year":"2024","unstructured":"Miah MSU, Kabir MM, Sarwar TB, Safran M, Alfarhood S, Mridha MF (2024) A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM. Sci Rep 14(1):1\u201318","journal-title":"Sci Rep"},{"key":"2787_CR97","doi-asserted-by":"publisher","first-page":"41021","DOI":"10.1007\/s11042-023-15213-3","volume":"82","author":"R Jain","year":"2023","unstructured":"Jain R, Singh R, Sajal R, Ruchir J, Jyoti A (2023) Real time sentiment analysis of natural language using multimedia input. Multimed Tools Appl 82:41021\u201341036","journal-title":"Multimed Tools Appl"},{"key":"2787_CR98","doi-asserted-by":"publisher","first-page":"2991","DOI":"10.56726\/irjmets46811","volume":"11","author":"O Access","year":"2023","unstructured":"Access O (2023) Ethical considerations in sentiment analysis: navigating the complex landscape. Int Res J Modern Eng Technol Sci 11:2991\u20133006. https:\/\/doi.org\/10.56726\/irjmets46811","journal-title":"Int Res J Modern Eng Technol Sci"},{"key":"2787_CR99","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1007\/s40747-024-01631-9","volume":"11","author":"L Khan","year":"2024","unstructured":"Khan L, Qazi A, Chang HT, Mahmood A (2024) Empowering Urdu sentiment analysis: an attention-based stacked CNN-Bi-LSTM DNN with multilingual BERT. Complex Intell Syst 11:10","journal-title":"Complex Intell Syst"},{"issue":"1","key":"2787_CR100","doi-asserted-by":"publisher","first-page":"9603","DOI":"10.1038\/s41598-024-60210-7","volume":"14","author":"MSU Miah","year":"2024","unstructured":"Miah MSU, Mohsin Kabir M, Sarwar TB, Safran M, Alfarhood S, Mridha MF (2024) A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM. Sci Rep 14(1):9603","journal-title":"Sci Rep"},{"key":"2787_CR101","doi-asserted-by":"publisher","unstructured":"Jardim S, Mora C, Santana T (2021) A multilingual Lexicon-based approach for sentiment analysis in social and cultural information system data. In: Iberian conference on information systems and technologies, CISTI https:\/\/doi.org\/10.23919\/CISTI52073.2021.9476631","DOI":"10.23919\/CISTI52073.2021.9476631"},{"key":"2787_CR102","doi-asserted-by":"crossref","unstructured":"Cambria E, Poria S, Hussain A, Gelbukh A (2017) SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. In: Thirty-First AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11559"},{"key":"2787_CR103","doi-asserted-by":"crossref","unstructured":"Liu B (2012) Sentiment analysis and opinion mining. Morgan Claypool Publishers","DOI":"10.1007\/978-3-031-02145-9"},{"key":"2787_CR104","doi-asserted-by":"crossref","unstructured":"Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing (EMNLP)","DOI":"10.3115\/1118693.1118704"},{"key":"2787_CR105","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems (NeurIPS)"},{"key":"2787_CR106","unstructured":"Jurafsky D, Martin J H (2023). Speech and Language Processing (3rd ed. draft). Stanford University. https:\/\/web.stanford.edu\/~jurafsky\/slp3\/"},{"issue":"2","key":"2787_CR107","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1162\/COLI_a_00049","volume":"37","author":"M Taboada","year":"2011","unstructured":"Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267\u2013307","journal-title":"Comput Linguist"},{"key":"2787_CR108","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1613\/jair.4992","volume":"57","author":"Y Goldberg","year":"2016","unstructured":"Goldberg Y (2016) A primer on neural network models for natural language processing. J Artif Intell Res 57:345\u2013420","journal-title":"J Artif Intell Res"},{"key":"2787_CR109","doi-asserted-by":"crossref","unstructured":"Chapelle O, Sch\u00f6lkopf B, Zien A (2006) Semi-supervised learning. MIT Press","DOI":"10.7551\/mitpress\/9780262033589.001.0001"},{"key":"2787_CR110","unstructured":"Sutskever et\u00a0al (2015) Web data mining. Unsupervised Learning, Springer (Computational Finance with R))"},{"key":"2787_CR111","doi-asserted-by":"crossref","unstructured":"Mohammad SM, Bravo-Marquez F (2017) WASSA-2017 shared task on emotion intensity, arXiv:1708.03700,","DOI":"10.18653\/v1\/W17-5205"},{"key":"2787_CR112","doi-asserted-by":"publisher","unstructured":"Kaur T, Gupta S (2021) Reputation management using sentiment analysis of online product reviews. In: 2021 11th International conference on cloud computing, data science and engineering (Confluence), Noida, India, pp. 704-709, https:\/\/doi.org\/10.1109\/Confluence51648.2021.9377048","DOI":"10.1109\/Confluence51648.2021.9377048"},{"issue":"5","key":"2787_CR113","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1080\/02699931.2013.767223","volume":"27","author":"KR Scherer","year":"2013","unstructured":"Scherer KR, Shuman V, Fontaine JRJ, Soriano C (2013) The GRID meets the wheel: assessing emotional experiences through direct and indirect measures. Cogn Emot 27(5):900\u2013923","journal-title":"Cogn Emot"},{"key":"2787_CR114","doi-asserted-by":"crossref","unstructured":"Xu H (2023a) Multimodal sentiment analysis. Singapore, Springer Nature Singapore, pp 217\u2013240","DOI":"10.1007\/978-981-99-5776-7_6"},{"key":"2787_CR115","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.inffus.2023.02.028","volume":"95","author":"L Zhu","year":"2023","unstructured":"Zhu L, Zhu Z, Zhang C, Xu Y, Kong X (2023) Multimodal sentiment analysis based on fusion methods: a survey. Inf Fusion 95:306\u2013325","journal-title":"Inf Fusion"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-026-02787-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-026-02787-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-026-02787-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T19:02:28Z","timestamp":1781031748000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-026-02787-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,9]]},"references-count":115,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["2787"],"URL":"https:\/\/doi.org\/10.1007\/s10115-026-02787-1","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,9]]},"assertion":[{"value":"8 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2026","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"180"}}