{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:49:12Z","timestamp":1771026552856,"version":"3.50.1"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-17437-9","type":"journal-article","created":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T05:01:39Z","timestamp":1700024499000},"page":"51755-51786","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["CBMAFM: CNN-BiLSTM Multi-Attention Fusion Mechanism for sentiment classification"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1053-014X","authenticated-orcid":false,"given":"Mayur","family":"Wankhade","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chandra Sekhara Rao","family":"Annavarapu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ajith","family":"Abraham","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"key":"17437_CR1","doi-asserted-by":"crossref","unstructured":"Wankhade M, Rao ACS, Kulkarni C (2022) A survey on sentiment analysis methods, applications, and challenges. Artif Intell Rev 1\u201350","DOI":"10.1007\/s10462-022-10144-1"},{"key":"17437_CR2","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.jss.2016.11.027","volume":"125","author":"N Genc-Nayebi","year":"2017","unstructured":"Genc-Nayebi N, Abran A (2017) A systematic literature review: Opinion mining studies from mobile app store user reviews. J Syst Softw 125:207\u2013219","journal-title":"J Syst Softw"},{"issue":"100","key":"17437_CR3","first-page":"413","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(100):413","journal-title":"Comput Sci Rev"},{"issue":"100","key":"17437_CR4","first-page":"979","volume":"68","author":"M Misuraca","year":"2021","unstructured":"Misuraca M, Scepi G, Spano M (2021) Using opinion mining as an educational analytic: An integrated strategy for the analysis of students\u2019 feedback. Stud Educ Eval 68(100):979","journal-title":"Stud Educ Eval"},{"issue":"10","key":"17437_CR5","doi-asserted-by":"publisher","first-page":"2026","DOI":"10.1109\/TKDE.2019.2913641","volume":"32","author":"L Wang","year":"2019","unstructured":"Wang L, Niu J, Yu S (2019) Sentidiff: combining textual information and sentiment diffusion patterns for twitter sentiment analysis. IEEE Trans Knowl Data Eng 32(10):2026\u20132039","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"17437_CR6","doi-asserted-by":"publisher","first-page":"37075","DOI":"10.1109\/ACCESS.2021.3062654","volume":"9","author":"Y Wang","year":"2021","unstructured":"Wang Y, Huang G, Li J, Li H, Zhou Y, Jiang H (2021) Refined global word embeddings based on sentiment concept for sentiment analysis. IEEE Access 9:37075\u201337085","journal-title":"IEEE Access"},{"key":"17437_CR7","unstructured":"Wang JH, Liu TW, Luo X, Wang L (2018) An LSTM approach to short text sentiment classification with word embeddings. In: Proceedings of the 30th conference on computational linguistics and speech processing (ROCLING 2018), pp 214\u2013223"},{"key":"17437_CR8","doi-asserted-by":"publisher","first-page":"71884","DOI":"10.1109\/ACCESS.2018.2878425","volume":"6","author":"X Fu","year":"2018","unstructured":"Fu X, Yang J, Li J, Fang M, Wang H (2018) Lexicon-enhanced LSTM with attention for general sentiment analysis. IEEE Access 6:71884\u201371891","journal-title":"IEEE Access"},{"key":"17437_CR9","doi-asserted-by":"crossref","unstructured":"Wankhade M, Annavarapu CSR, Abraham A (2023) Mapa bilstm-bert: multi-aspects position aware attention for aspect level sentiment analysis. J Supercomput 79(10):11452\u201311477","DOI":"10.1007\/s11227-023-05112-7"},{"key":"17437_CR10","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.neucom.2018.04.045","volume":"308","author":"G Rao","year":"2018","unstructured":"Rao G, Huang W, Feng Z, Cong Q (2018) Lstm with sentence representations for document-level sentiment classification. Neurocomputing 308:49\u201357","journal-title":"Neurocomputing"},{"issue":"45","key":"17437_CR11","doi-asserted-by":"publisher","first-page":"34407","DOI":"10.1007\/s11042-020-09198-6","volume":"79","author":"B Stuner","year":"2020","unstructured":"Stuner B, Chatelain C, Paquet T (2020) Handwriting recognition using cohort of LSTM and lexicon verification with extremely large lexicon. Multimedia Tools Appl 79(45):34407\u201334427","journal-title":"Multimedia Tools Appl"},{"key":"17437_CR12","doi-asserted-by":"publisher","first-page":"38979","DOI":"10.1109\/ACCESS.2021.3059342","volume":"9","author":"P Dansena","year":"2021","unstructured":"Dansena P, Bag S, Pal R (2021) Generation of synthetic data for handwritten word alteration detection. IEEE Access 9:38979\u201338990","journal-title":"IEEE Access"},{"key":"17437_CR13","doi-asserted-by":"crossref","unstructured":"Zhu X, Guo K, Ren S, Hu B, Hu M, Fang H (2021) Lightweight image super-resolution with expectation-maximization attention mechanism. IEEE Trans Circ Syst Video Technol","DOI":"10.1109\/TCSVT.2021.3078436"},{"key":"17437_CR14","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.knosys.2019.01.028","volume":"169","author":"Y Song","year":"2019","unstructured":"Song Y, Hu QV, He L (2019) P-cnn: Enhancing text matching with positional convolutional neural network. Knowl-Based Syst 169:67\u201379","journal-title":"Knowl-Based Syst"},{"key":"17437_CR15","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1109\/ACCESS.2018.2885362","volume":"7","author":"X Zhang","year":"2018","unstructured":"Zhang X, Huang S, Zhao J, Du X, He F (2018) Exploring deep recurrent convolution neural networks for subjectivity classification. IEEE Access 7:347\u2013357","journal-title":"IEEE Access"},{"key":"17437_CR16","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.078","volume":"337","author":"G Liu","year":"2019","unstructured":"Liu G, Guo J (2019) Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing 337:325\u2013338","journal-title":"Neurocomputing"},{"key":"17437_CR17","doi-asserted-by":"crossref","unstructured":"Wankhade M, Annavarapu CSR, Verma MK (2021) Cbvosd: context based vectors over sentiment domain ensemble model for review classification. J Supercomput 1\u201337","DOI":"10.1007\/s11227-021-04132-5"},{"key":"17437_CR18","doi-asserted-by":"crossref","unstructured":"Wankhade M, Rao ACS (2022) Bi-directional lstm attention mechanism for sentiment classification. In: 2022 2nd Asian Conference on Innovation in Technology (ASIANCON), IEEE, pp 1\u20136","DOI":"10.1109\/ASIANCON55314.2022.9908909"},{"issue":"5","key":"17437_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457206","volume":"20","author":"PK Jain","year":"2021","unstructured":"Jain PK, Saravanan V, Pamula R (2021) A hybrid CNN-LSTM: A deep learning approach for consumer sentiment analysis using qualitative user-generated contents. Trans Asian Low-Resour Lang Inf Process 20(5):1\u201315","journal-title":"Trans Asian Low-Resour Lang Inf Process"},{"key":"17437_CR20","unstructured":"Angiani G, Ferrari L, Fontanini T, Fornacciari P, Iotti E, Magliani F, Manicardi S (2016) A comparison between preprocessing techniques for sentiment analysis in twitter. In: KDWeb"},{"issue":"2","key":"17437_CR21","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":"17437_CR22","doi-asserted-by":"crossref","unstructured":"Deng X, Li Y, Weng J, Zhang J (2019) Feature selection for text classification: A review. Multimedia Tools Appl 78(3)","DOI":"10.1007\/s11042-018-6083-5"},{"key":"17437_CR23","doi-asserted-by":"crossref","unstructured":"Saad SE, Yang J (2019) Twitter sentiment analysis based on ordinal regression. IEEE Access 7:163677\u2013163685","DOI":"10.1109\/ACCESS.2019.2952127"},{"key":"17437_CR24","doi-asserted-by":"crossref","unstructured":"Dhal P, Azad C (2021) A comprehensive survey on feature selection in the various fields of machine learning. Appl Intell 1\u201339","DOI":"10.1007\/s10489-021-02550-9"},{"key":"17437_CR25","doi-asserted-by":"publisher","first-page":"32664","DOI":"10.1109\/ACCESS.2019.2903331","volume":"7","author":"YA Alhaj","year":"2019","unstructured":"Alhaj YA, Xiang J, Zhao D, Al-Qaness MA, Abd Elaziz M, Dahou A (2019) A study of the effects of stemming strategies on Arabic document classification. IEEE Access 7:32664\u201332671","journal-title":"IEEE Access"},{"issue":"2","key":"17437_CR26","doi-asserted-by":"publisher","first-page":"27","DOI":"10.3390\/a9020027","volume":"9","author":"A Ayedh","year":"2016","unstructured":"Ayedh A, Tan G, Alwesabi K, Rajeh H (2016) The effect of preprocessing on Arabic document categorization. Algorithms 9(2):27","journal-title":"Algorithms"},{"key":"17437_CR27","first-page":"1","volume-title":"2018 International Symposium on Agent","author":"MA Mohammed","year":"2018","unstructured":"Mohammed MA, Gunasekaran SS, Mostafa SA, Mustafa A, Abd Ghani MK (2018) Implementing an agent-based multi-natural language anti-spam model. 2018 International Symposium on Agent. Multi-Agent Systems and Robotics (ISAMSR), IEEE, pp 1\u20135"},{"key":"17437_CR28","doi-asserted-by":"crossref","unstructured":"Dos\u00a0Santos FL, Ladeira M (2014) The role of text pre-processing in opinion mining on a social media language dataset. In: 2014 Brazilian Conference on Intelligent Systems, IEEE, pp 50\u201354","DOI":"10.1109\/BRACIS.2014.20"},{"key":"17437_CR29","unstructured":"Wankhade M, Rao ACS, Dara S, Kaushik B (2017) A sentiment analysis of food review using logistic regression. In: International Conference on Machine Learning and Computational Intelligence-2017, pp 2456\u20133307"},{"issue":"1","key":"17437_CR30","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1007\/s12559-022-10083-7","volume":"15","author":"C Yan","year":"2023","unstructured":"Yan C, Liu J, Liu W, Liu X (2023) Sentiment analysis and topic mining using a novel deep attention-based parallel dual-channel model for online course reviews. Cogn Comput 15(1):304\u2013322","journal-title":"Cogn Comput"},{"key":"17437_CR31","doi-asserted-by":"crossref","unstructured":"Wankhade M, Rao ACS (2022) Opinion analysis and aspect understanding during covid-19 pandemic using BERT-Bi-LSTM ensemble method. Sci Rep 12(1):17,095","DOI":"10.1038\/s41598-022-21604-7"},{"key":"17437_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107397","volume":"95","author":"PK Jain","year":"2021","unstructured":"Jain PK, Yekun EA, Pamula R, Srivastava G (2021) Consumer recommendation prediction in online reviews using cuckoo optimized machine learning models. Comput Electr Eng 95:107397","journal-title":"Comput Electr Eng"},{"key":"17437_CR33","doi-asserted-by":"publisher","first-page":"132970","DOI":"10.1109\/ACCESS.2020.3010802","volume":"8","author":"J Zhou","year":"2020","unstructured":"Zhou J, Jin S, Huang X (2020) ADeCNN: An improved model for aspect-level sentiment analysis based on deformable CNN and attention. IEEE Access 8:132970\u2013132979","journal-title":"IEEE Access"},{"key":"17437_CR34","doi-asserted-by":"crossref","unstructured":"Lynch C, O\u2019Leary C, Smith G, Bain R, Kehoe J, Vakaloudis A, Linger R (2020) A review of open-source machine learning algorithms for twitter text sentiment analysis and image classification. In: 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1\u20139","DOI":"10.1109\/IJCNN48605.2020.9207544"},{"key":"17437_CR35","doi-asserted-by":"crossref","unstructured":"Jain DK, Boyapati P, Venkatesh J, Prakash M (2022) An intelligent cognitive-inspired computing with big data analytics framework for sentiment analysis and classification. Inf Process Manag 59(1):102758","DOI":"10.1016\/j.ipm.2021.102758"},{"key":"17437_CR36","doi-asserted-by":"crossref","unstructured":"Krouska A, Troussas C, Virvou M (2016) The effect of preprocessing techniques on twitter sentiment analysis. In: 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA), IEEE, pp 1\u20135","DOI":"10.1109\/IISA.2016.7785373"},{"issue":"2","key":"17437_CR37","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1016\/j.eswa.2012.07.059","volume":"40","author":"R Moraes","year":"2013","unstructured":"Moraes R, Valiati JF, Neto WPG (2013) Document-level sentiment classification: An empirical comparison between SVM and ANN. Expert Syst Appl 40(2):621\u2013633","journal-title":"Expert Syst Appl"},{"key":"17437_CR38","unstructured":"Gong J, Qiu X, Wang S, Huang X (2018) Information aggregation via dynamic routing for sequence encoding. arXiv preprint arXiv:1806.01501"},{"key":"17437_CR39","doi-asserted-by":"crossref","unstructured":"Yurtsever MME, Shiraz M, Ekinci E, Eken S (2023) Comparing COVID-19 vaccine passports attitudes across countries by analysing Reddit comments. J Inf Sci 01655515221148356","DOI":"10.1177\/01655515221148356"},{"key":"17437_CR40","doi-asserted-by":"crossref","unstructured":"Omurca SI, Ekinci E, Sevim S, Edinc EB, Eken S, Sayar A (2023) A document image classification system fusing deep and machine learning models. Appl Intell 53(12):15295\u201315310","DOI":"10.1007\/s10489-022-04306-5"},{"issue":"10","key":"17437_CR41","doi-asserted-by":"publisher","first-page":"2335","DOI":"10.3390\/math11102335","volume":"11","author":"D Zeng","year":"2023","unstructured":"Zeng D, Chen X, Song Z, Xue Y, Cai Q (2023) Multimodal interaction and fused graph convolution network for sentiment classification of online reviews. Mathematics 11(10):2335","journal-title":"Mathematics"},{"issue":"7","key":"17437_CR42","doi-asserted-by":"publisher","first-page":"4367","DOI":"10.1007\/s10489-020-02116-1","volume":"51","author":"Y Chen","year":"2021","unstructured":"Chen Y, Liu L, Phonevilay V, Gu K, Xia R, Xie J, Zhang Q, Yang K (2021) Image super-resolution reconstruction based on feature map attention mechanism. Appl Intell 51(7):4367\u20134380","journal-title":"Appl Intell"},{"key":"17437_CR43","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.neucom.2020.01.006","volume":"387","author":"W Li","year":"2020","unstructured":"Li W, Qi F, Tang M, Yu Z (2020) Bidirectional LSTM with self-attention mechanism and multi-channel features for sentiment classification. Neurocomputing 387:63\u201377","journal-title":"Neurocomputing"},{"key":"17437_CR44","doi-asserted-by":"crossref","unstructured":"Tang D, Qin B, Liu T (2016) Aspect level sentiment classification with deep memory network. arXiv preprint arXiv:1605.08900","DOI":"10.18653\/v1\/D16-1021"},{"key":"17437_CR45","doi-asserted-by":"crossref","unstructured":"Jain PK, Pamula R, Yekun EA (2022) A multi-label ensemble predicting model to service recommendation from social media contents. J Supercomput 1\u201318","DOI":"10.1007\/s11227-021-04087-7"},{"key":"17437_CR46","doi-asserted-by":"crossref","unstructured":"Zhang M, Zhang Y, Vo DT (2016) Gated neural networks for targeted sentiment analysis. In: Thirtieth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v30i1.10380"},{"key":"17437_CR47","doi-asserted-by":"crossref","unstructured":"Ambartsoumian A, Popowich F (2018) Self-attention: A better building block for sentiment analysis neural network classifiers. arXiv preprint arXiv:1812.07860","DOI":"10.18653\/v1\/W18-6219"},{"issue":"3","key":"17437_CR48","first-page":"1","volume":"7","author":"Z Li","year":"2016","unstructured":"Li Z, Tang J, Wang X, Liu J, Lu H (2016) Multimedia news summarization in search. ACM Trans Intell Syst Technol (TIST) 7(3):1\u201320","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"17437_CR49","unstructured":"Tang D, Qin B, Feng X, Liu T (2015) Effective lstms for target-dependent sentiment classification. arXiv preprint arXiv:1512.01100"},{"key":"17437_CR50","doi-asserted-by":"crossref","unstructured":"Wang S, Mazumder S, Liu B, Zhou M, Chang Y (2018) Target-sensitive memory networks for aspect sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","DOI":"10.18653\/v1\/P18-1088"},{"issue":"3","key":"17437_CR51","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MCI.2016.2572539","volume":"11","author":"T Chen","year":"2016","unstructured":"Chen T, Xu R, He Y, Xia Y, Wang X (2016) Learning user and product distributed representations using a sequence model for sentiment analysis. IEEE Comput Intell Mag 11(3):34\u201344","journal-title":"IEEE Comput Intell Mag"},{"issue":"3","key":"17437_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3487042","volume":"18","author":"Y Pan","year":"2022","unstructured":"Pan Y, Li Z, Zhang L, Tang J (2022) Causal inference with knowledge distilling and curriculum learning for unbiased VQA. ACM Trans Multimed Comput Commun Appl (TOMM) 18(3):1\u201323","journal-title":"ACM Trans Multimed Comput Commun Appl (TOMM)"},{"key":"17437_CR53","doi-asserted-by":"crossref","unstructured":"Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 1480\u20131489","DOI":"10.18653\/v1\/N16-1174"},{"key":"17437_CR54","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. Adv Neural Inf Process Syst 30"},{"key":"17437_CR55","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","volume":"452","author":"Z Niu","year":"2021","unstructured":"Niu Z, Zhong G, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48\u201362","journal-title":"Neurocomputing"},{"key":"17437_CR56","doi-asserted-by":"crossref","unstructured":"He R, McAuley J (2016) Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In: proceedings of the 25th international conference on world wide web, pp 507\u2013517","DOI":"10.1145\/2872427.2883037"},{"issue":"2","key":"17437_CR57","first-page":"269","volume":"015","author":"S Loria","year":"2018","unstructured":"Loria S (2018) textblob documentation. Release 015(2):269","journal-title":"Release"},{"key":"17437_CR58","doi-asserted-by":"crossref","unstructured":"Jianqiang Z (2015) Pre-processing boosting twitter sentiment analysis? In: 2015 IEEE International Conference on Smart City\/SocialCom\/SustainCom (SmartCity), IEEE, pp 748\u2013753","DOI":"10.1109\/SmartCity.2015.158"},{"key":"17437_CR59","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.chb.2018.12.029","volume":"93","author":"A Chatterjee","year":"2019","unstructured":"Chatterjee A, Gupta U, Chinnakotla MK, Srikanth R, Galley M, Agrawal P (2019) Understanding emotions in text using deep learning and big data. Comput Hum Behav 93:309\u2013317","journal-title":"Comput Hum Behav"},{"key":"17437_CR60","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.eswa.2018.08.044","volume":"117","author":"SM Rezaeinia","year":"2019","unstructured":"Rezaeinia SM, Rahmani R, Ghodsi A, Veisi H (2019) Sentiment analysis based on improved pre-trained word embeddings. Expert Syst Appl 117:139\u2013147","journal-title":"Expert Syst Appl"},{"issue":"5","key":"17437_CR61","doi-asserted-by":"publisher","first-page":"6411","DOI":"10.1007\/s11227-021-04132-5","volume":"78","author":"M Wankhade","year":"2022","unstructured":"Wankhade M, Annavarapu CSR, Verma MK (2022) CBVoSD: context based vectors over sentiment domain ensemble model for review classification. J Supercomput 78(5):6411\u20136447","journal-title":"J Supercomput"},{"key":"17437_CR62","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.procs.2013.05.005","volume":"17","author":"E Haddi","year":"2013","unstructured":"Haddi E, Liu X, Shi Y (2013) The role of text pre-processing in sentiment analysis. Procedia Comput Sci 17:26\u201332","journal-title":"Procedia Comput Sci"},{"key":"17437_CR63","doi-asserted-by":"crossref","unstructured":"Bao Y, Quan C, Wang L, Ren F (2014) The role of pre-processing in twitter sentiment analysis. In: International conference on intelligent computing, Springer, pp 615\u2013624","DOI":"10.1007\/978-3-319-09339-0_62"},{"key":"17437_CR64","doi-asserted-by":"crossref","unstructured":"Solakidis GS, Vavliakis KN, Mitkas PA (2014) Multilingual sentiment analysis using emoticons and keywords. In: 2014 IEEE\/WIC\/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), IEEE, vol\u00a02, pp 102\u2013109","DOI":"10.1109\/WI-IAT.2014.86"},{"key":"17437_CR65","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.eswa.2017.02.002","volume":"77","author":"O Araque","year":"2017","unstructured":"Araque O, Corcuera-Platas I, S\u00e1nchez-Rada JF, Iglesias CA (2017) Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst Appl 77:236\u2013246","journal-title":"Expert Syst Appl"},{"key":"17437_CR66","doi-asserted-by":"crossref","unstructured":"Datar M, Kosamkar P (2016) A novel approach for polarity determination using emoticons: emoticon-graph. In: Proceedings of International Conference on ICT for Sustainable Development, Springer, pp 481\u2013489","DOI":"10.1007\/978-981-10-0135-2_47"},{"issue":"4","key":"17437_CR67","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1016\/j.ipm.2017.02.004","volume":"53","author":"AC Pandey","year":"2017","unstructured":"Pandey AC, Rajpoot DS, Saraswat M (2017) Twitter sentiment analysis using hybrid cuckoo search method. Inf Process Manag 53(4):764\u2013779","journal-title":"Inf Process Manag"},{"key":"17437_CR68","unstructured":"Heidarysafa M, Kowsari K, Brown DE, Meimandi KJ, Barnes LE (2018) An improvement of data classification using random multimodel deep learning (RMDL). arXiv preprint arXiv:1808.08121"},{"key":"17437_CR69","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.chb.2019.05.023","volume":"99","author":"MA Coyle","year":"2019","unstructured":"Coyle MA, Carmichael CL (2019) Perceived responsiveness in text messaging: The role of emoji use. Comput Hum Behav 99:181\u2013189","journal-title":"Comput Hum Behav"},{"key":"17437_CR70","doi-asserted-by":"crossref","unstructured":"Pradhan A, Senapati MR, Sahu PK (2021) Improving sentiment analysis with learning concepts from concept, patterns lexicons and negations. Ain Shams Eng J","DOI":"10.1016\/j.asej.2021.08.004"},{"key":"17437_CR71","doi-asserted-by":"crossref","unstructured":"Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075","DOI":"10.3115\/v1\/P15-1150"},{"key":"17437_CR72","doi-asserted-by":"crossref","unstructured":"Ruder S, Ghaffari P, Breslin JG (2016) Insight-1 at semeval-2016 task 4: convolutional neural networks for sentiment classification and quantification. arXiv preprint arXiv:1609.02746","DOI":"10.18653\/v1\/S16-1026"},{"key":"17437_CR73","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.ins.2016.09.002","volume":"373","author":"F Wu","year":"2016","unstructured":"Wu F, Song Y, Huang Y (2016) Microblog sentiment classification with heterogeneous sentiment knowledge. Inf Sci 373:149\u2013164","journal-title":"Inf Sci"},{"key":"17437_CR74","doi-asserted-by":"crossref","unstructured":"Jain D, Garg A, Saraswat M (2019) Sentiment analysis using few short learning. In: 2019 Fifth International Conference on Image Information Processing (ICIIP), IEEE, pp 102\u2013107","DOI":"10.1109\/ICIIP47207.2019.8985855"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17437-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17437-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17437-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T07:44:02Z","timestamp":1715759042000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17437-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,15]]},"references-count":74,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["17437"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17437-9","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,15]]},"assertion":[{"value":"16 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}