{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T03:41:48Z","timestamp":1773286908506,"version":"3.50.1"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"vor","delay-in-days":9,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Vellore Institute of Technology, Vellore"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-026-01244-9","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T07:05:19Z","timestamp":1772435119000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Intelligent Bi-directional Gate Recurrent Neural Network Based Hybrid Deep Learning Model for Text-based Sentiment Analysis"],"prefix":"10.1007","volume":"19","author":[{"given":"M","family":"Selvi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"SVN Santhosh","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"issue":"1","key":"1244_CR1","doi-asserted-by":"publisher","first-page":"49","DOI":"10.26650\/jot.2022.8.1.1038566","volume":"8","author":"S Cherdouh","year":"2022","unstructured":"Cherdouh, S., Kherri, A., Abbaci, A., Kebir, S.: Using Sentiment Analysis of Online Hotel Reviews To Explore the Effect of Information and Communication Technologies on Hotel Guest Satisfaction. J. Tourismology. 8(1), 49\u201367 (2022)","journal-title":"J. Tourismology"},{"issue":"12","key":"1244_CR2","doi-asserted-by":"publisher","first-page":"2286","DOI":"10.3390\/pr9122286","volume":"9","author":"A Amjad","year":"2021","unstructured":"Amjad, A., Khan, L., Chang, H.T.: Semi-natural and spontaneous speech recognition using deep neural networks with hybrid features unification. Processes. 9(12), 2286 (2021)","journal-title":"Processes"},{"issue":"1","key":"1244_CR3","first-page":"28","volume":"90","author":"C Shirky","year":"2011","unstructured":"Shirky, C.: The Political Power of Social Media: Technology, the Public Sphere, and Political Change. Foreign Aff. 90(1), 28\u201341 (2011). http:\/\/www.jstor.org\/stable\/25800379","journal-title":"Foreign Aff."},{"issue":"1","key":"1244_CR4","first-page":"65","volume":"22","author":"RV Dixit","year":"2018","unstructured":"Dixit, R.V., Prakash, G.: Intentions to use social networking sites (SNS) using technology acceptance model (TAM) an empirical study. Paradigm. 22(1), 65\u201379 (2018)","journal-title":"Paradigm"},{"issue":"2","key":"1244_CR5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.2753\/JEC1086-4415160201","volume":"16","author":"TP Liang","year":"2011","unstructured":"Liang, T.P., Turban, E.: Introduction to the special issue social commerce: a research framework for social commerce. Int. J. Electron. Commer. 16(2), 5\u201314 (2011)","journal-title":"Int. J. Electron. Commer."},{"key":"1244_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.datak.2012.02.005","volume":"74","author":"A Reyes","year":"2012","unstructured":"Reyes, A., Rosso, P., Buscaldi, D.: From humor recognition to irony detection: The figurative language of social media. Data Knowl. Eng. 74, 1\u201312 (2012)","journal-title":"Data Knowl. Eng."},{"issue":"3","key":"1244_CR7","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1093\/comjnl\/bxz031","volume":"63","author":"G Liu","year":"2020","unstructured":"Liu, G., Huang, X., Liu, X., Yang, A.: A novel aspect-based sentiment analysis network model based on multilingual hierarchy in online social network. Comput. J. 63(3), 410\u2013424 (2020)","journal-title":"Comput. J."},{"key":"1244_CR8","doi-asserted-by":"publisher","first-page":"101724","DOI":"10.1016\/j.techsoc.2021.101724","volume":"67","author":"X Dong","year":"2021","unstructured":"Dong, X., Lian, Y.: A review of social media-based public opinion analyses: Challenges and recommendations. Technol. Soc. 67, 101724 (2021)","journal-title":"Technol. Soc."},{"key":"1244_CR9","doi-asserted-by":"publisher","unstructured":"Sharma, D., Sabharwal, M., Goyal, V., Vij, M.: Sentiment Analysis Techniques for Social Media Data: A Review. In: Luhach, A., Kosa, J., Poonia, R., Gao, XZ., Singh, D. (eds) First International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1045. Springer, Singapore. (2020). https:\/\/doi.org\/10.1007\/978-981-15-0029-9_7","DOI":"10.1007\/978-981-15-0029-9_7"},{"issue":"5","key":"1244_CR10","doi-asserted-by":"publisher","first-page":"1660","DOI":"10.18517\/ijaseit.7.5.2137","volume":"7","author":"B Saberi","year":"2017","unstructured":"Saberi, B., Saad, S.: Sentiment analysis or opinion mining: A review. Int. J. Adv. Sci. Eng. Inf. Technol. 7(5), 1660\u20131666 (2017)","journal-title":"Int. J. Adv. Sci. Eng. Inf. Technol."},{"key":"1244_CR11","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s10796-018-9867-2","volume":"22","author":"S Stieglitz","year":"2020","unstructured":"Stieglitz, S., Meske, C., Ross, B., Mirbabaie, M.: Going back in time to predict the future-the complex role of the data collection period in social media analytics. Inform. Syst. Front. 22, 395\u2013409 (2020)","journal-title":"Inform. Syst. Front."},{"issue":"4","key":"1244_CR12","doi-asserted-by":"publisher","first-page":"3247","DOI":"10.11591\/ijece.v9i4.pp3247-3255","volume":"9","author":"JJ Stephen","year":"2019","unstructured":"Stephen, J.J., Prabu, P.: Detecting the magnitude of depression in Twitter users using sentiment analysis. Int. J. Electr. Comput. Eng. 9(4), 3247\u20133255 (2019)","journal-title":"Int. J. Electr. Comput. Eng."},{"issue":"Suppl 1","key":"1244_CR13","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1007\/s13198-021-01379-2","volume":"13","author":"VV Kumar","year":"2022","unstructured":"Kumar, V.V., Raghunath, K.K., Muthukumaran, V., Joseph, R.B., Beschi, I.S., Uday, A.K.: Aspect based sentiment analysis and smart classification in uncertain feedback pool. Int. J. Syst. Assur. Eng. Manage. 13(Suppl 1), 252\u2013262 (2022)","journal-title":"Int. J. Syst. Assur. Eng. Manage."},{"key":"1244_CR14","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.eswa.2016.10.043","volume":"69","author":"M Giatsoglou","year":"2017","unstructured":"Giatsoglou, M., Vozalis, M.G., Diamantaras, K., Vakali, A., Sarigiannidis, G., Chatzisavvas, K.C.: Sentiment analysis leveraging emotions and word embeddings. Expert Syst. Appl. 69, 214\u2013224 (2017)","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"1244_CR15","first-page":"156","volume":"2","author":"NZT Abdulnabi","year":"2016","unstructured":"Abdulnabi, N.Z.T., Altun, O.: Batch size for training convolutional neural networks for sentence classification. J. Adv. Technol. Eng. Stud. 2(5), 156\u2013163 (2016)","journal-title":"J. Adv. Technol. Eng. Stud."},{"key":"1244_CR16","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.ins.2016.06.040","volume":"369","author":"Y Ren","year":"2016","unstructured":"Ren, Y., Wang, R., Ji, D.: A topic-enhanced word embedding for Twitter sentiment classification. Inf. Sci. 369, 188\u2013198 (2016)","journal-title":"Inf. Sci."},{"issue":"3","key":"1244_CR17","doi-asserted-by":"publisher","first-page":"697","DOI":"10.32604\/cmc.2019.05375","volume":"58","author":"F Xu","year":"2019","unstructured":"Xu, F., Zhang, X., Xin, Z., Yang, A.: Investigation on the Chinese text sentiment analysis based on convolutional neural networks in deep learning. Computers Mater. Continua. 58(3), 697\u2013709 (2019)","journal-title":"Computers Mater. Continua"},{"key":"1244_CR18","doi-asserted-by":"publisher","first-page":"13949","DOI":"10.1109\/ACCESS.2018.2814818","volume":"6","author":"A Hassan","year":"2018","unstructured":"Hassan, A., Mahmood, A.: Convolutional recurrent deep learning model for sentence classification. IEEE Access. 6, 13949\u201313957 (2018)","journal-title":"IEEE Access."},{"issue":"6","key":"1244_CR19","first-page":"292","volume":"5","author":"D Tang","year":"2015","unstructured":"Tang, D., Qin, B., Liu, T.: Deep learning for sentiment analysis: successful approaches and future challenges. Wiley Interdisciplinary Reviews: Data Min. Knowl. Discovery. 5(6), 292\u2013303 (2015)","journal-title":"Wiley Interdisciplinary Reviews: Data Min. Knowl. Discovery"},{"issue":"2","key":"1244_CR20","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1109\/TNNLS.2020.2979670","volume":"32","author":"DW Otter","year":"2020","unstructured":"Otter, D.W., Medina, J.R., Kalita, J.K.: A survey of the usages of deep learning for natural language processing. IEEE Trans. neural networks Learn. Syst. 32(2), 604\u2013624 (2020)","journal-title":"IEEE Trans. neural networks Learn. Syst."},{"key":"1244_CR21","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.procs.2019.08.153","volume":"157","author":"D Jatnika","year":"2019","unstructured":"Jatnika, D., Bijaksana, M.A., Suryani, A.A.: Word2vec model analysis for semantic similarities in english words. Procedia Comput. Sci. 157, 160\u2013167 (2019)","journal-title":"Procedia Comput. Sci."},{"issue":"1","key":"1244_CR22","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1186\/s40537-023-00781-w","volume":"10","author":"AS Talaat","year":"2023","unstructured":"Talaat, A.S.: Sentiment analysis classification system using hybrid BERT models. J. Big Data. 10(1), 110 (2023)","journal-title":"J. Big Data"},{"issue":"5","key":"1244_CR23","first-page":"495","volume":"8","author":"AH Jadidinejad","year":"2015","unstructured":"Jadidinejad, A.H., Sadr, H.: Improving weak queries using local cluster analysis as a preliminary framework. Indian J. Sci. Technol. 8(5), 495\u2013510 (2015)","journal-title":"Indian J. Sci. Technol."},{"key":"1244_CR24","doi-asserted-by":"crossref","unstructured":"Saberi, Z.A., Sadr, H., Yamaghani, M.R.: An Intelligent Diagnosis System for Predicting Coronary Heart Disease. In 2024 10th International Conference on Artificial Intelligence and Robotics (QICAR). IEEE,131\u2013137 ,(2024)","DOI":"10.1109\/QICAR61538.2024.10496601"},{"issue":"7","key":"1244_CR25","doi-asserted-by":"publisher","first-page":"7647","DOI":"10.1007\/s10489-022-03907-4","volume":"53","author":"A Pradhan","year":"2023","unstructured":"Pradhan, A., Ranjan Senapati, M., Sahu, P.K.: A multichannel embedding and arithmetic optimized stacked Bi-GRU model with semantic attention to detect emotion over text data. Appl. Intell. 53(7), 7647\u20137664 (2023)","journal-title":"Appl. Intell."},{"issue":"1","key":"1244_CR26","first-page":"6287559","volume":"2022","author":"M Bhakuni","year":"2022","unstructured":"Bhakuni, M., Kumar, K., Sonia, Iwendi, C., Singh, A.: Evolution and evaluation: Sarcasm analysis for twitter data using sentiment analysis. J. Sens. 2022(1), 6287559 (2022)","journal-title":"J. Sens."},{"issue":"1","key":"1244_CR27","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1186\/s40001-024-02044-7","volume":"29","author":"H Sadr","year":"2024","unstructured":"Sadr, H., Salari, A., Ashoobi, M.T., Nazari, M.: Cardiovascular disease diagnosis: a holistic approach using the integration of machine learning and deep learning models. Eur. J. Med. Res. 29(1), 455 (2024)","journal-title":"Eur. J. Med. Res."},{"key":"1244_CR28","first-page":"3111","volume":"26","author":"T Mikolov","year":"2013","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural. Inf. Process. Syst. 26, 3111\u20133119 (2013)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1244_CR29","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP),1532\u20131543, (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"1244_CR30","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski, P., Grave, E., Joulin, A., Mikolov: T. Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135\u2013146 (2017). [CrossRef]","journal-title":"Trans. Assoc. Comput. Linguist"},{"issue":"1","key":"1244_CR31","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1080\/0144929X.2022.2156387","volume":"43","author":"A Alslaity","year":"2024","unstructured":"Alslaity, A., Orji, R.: Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directions. Behav. Inform. Technol. 43(1), 139\u2013164 (2024)","journal-title":"Behav. Inform. Technol."},{"issue":"9","key":"1244_CR32","doi-asserted-by":"publisher","first-page":"25769","DOI":"10.1007\/s11042-023-16488-2","volume":"83","author":"Z Khodaverdian","year":"2024","unstructured":"Khodaverdian, Z., Sadr, H., Edalatpanah, S.A., Nazari, M.: An energy aware resource allocation based on combination of CNN and GRU for virtual machine selection. Multimedia Tools Appl. 83(9), 25769\u201325796 (2024)","journal-title":"Multimedia Tools Appl."},{"key":"1244_CR33","doi-asserted-by":"crossref","unstructured":"Sadr, H., Soleimandarabi, M.N., Pedram, M., Teshnelab, M.: Unified topic-based semantic models: a study in computing the semantic relatedness of geographic terms. In 2019 5th International Conference on Web Research (ICWR) . IEEE,134\u2013140, (2019)","DOI":"10.1109\/ICWR.2019.8765257"},{"issue":"3","key":"1244_CR34","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1162\/dint_a_00147","volume":"4","author":"YY Zhang","year":"2022","unstructured":"Zhang, Y.Y., Chen, Y., Yu, S., Gu, X., Song, M., Peng, Y., Chen, J., Liu, Q.: Bi-GRU Relation Extraction Model Based on Keywords Attention. Data Intell. 4(3), 552\u2013572 (2022). https:\/\/doi.org\/10.1162\/dint_a_00147","journal-title":"Data Intell."},{"issue":"3","key":"1244_CR35","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1007\/s13042-022-01670-z","volume":"14","author":"A Hosseinalipour","year":"2023","unstructured":"Hosseinalipour, A., Ghanbarzadeh, R.: A novel metaheuristic optimisation approach for text sentiment analysis. Int. J. Mach. Learn. Cybernet. 14(3), 889\u2013909 (2023)","journal-title":"Int. J. Mach. Learn. Cybernet."},{"key":"1244_CR36","doi-asserted-by":"publisher","first-page":"103694","DOI":"10.1109\/ACCESS.2022.3210182","volume":"10","author":"KL Tan","year":"2022","unstructured":"Tan, K.L., Lee, C.P., Lim, K.M., Anbananthen, K.S.M.: Sentiment analysis with ensemble hybrid deep learning model. IEEE Access. 10, 103694\u2013103704 (2022)","journal-title":"IEEE Access."},{"issue":"1","key":"1244_CR37","doi-asserted-by":"publisher","first-page":"9986920","DOI":"10.1155\/2021\/9986920","volume":"2021","author":"CN Dang","year":"2021","unstructured":"Dang, C.N., Moreno-Garc\u00eda, M.N., De la Prieta, F.: Hybrid deep learning models for sentiment analysis. Complexity. 2021(1), 9986920 (2021)","journal-title":"Complexity"},{"issue":"5","key":"1244_CR38","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1007\/s10796-021-10107-x","volume":"23","author":"S Mendon","year":"2021","unstructured":"Mendon, S., Dutta, P., Behl, A., Lessmann, S.: A hybrid approach of machine learning and lexicons to sentiment analysis: enhanced insights from twitter data of natural disasters. Inform. Syst. Front. 23(5), 1145\u20131168 (2021)","journal-title":"Inform. Syst. Front."},{"key":"1244_CR39","first-page":"100619","volume":"25","author":"J Sangeetha","year":"2023","unstructured":"Sangeetha, J., Kumaran, U.: A hybrid optimization algorithm using BiLSTM structure for sentiment analysis. Measurement: Sens. 25, 100619 (2023)","journal-title":"Measurement: Sens."},{"issue":"14","key":"1244_CR40","doi-asserted-by":"publisher","first-page":"10311","DOI":"10.1007\/s00521-023-08236-2","volume":"35","author":"S Aslan","year":"2023","unstructured":"Aslan, S., K\u0131z\u0131loluk, S., Sert, E.: TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm. Neural Comput. Appl. 35(14), 10311\u201310328 (2023)","journal-title":"Neural Comput. Appl."},{"key":"1244_CR41","doi-asserted-by":"crossref","unstructured":"Ali Al-Abyadh, M.H., Iesa, M.A., Abdel Azeem, H., Singh, H.A., Kumar, D.P., Abdulamir, P., M., Jalali, A.: Deep sentiment analysis of twitter data using a hybrid ghost convolution neural network Model. Computational Intelligence and Neuroscience, 2022(1), 659-5799. (2022)","DOI":"10.1155\/2022\/6595799"},{"issue":"2","key":"1244_CR42","doi-asserted-by":"publisher","first-page":"2499","DOI":"10.1007\/s13369-021-06227-w","volume":"47","author":"A Alsayat","year":"2022","unstructured":"Alsayat, A.: Improving sentiment analysis for social media applications using an ensemble deep learning language model. Arab. J. Sci. Eng. 47(2), 2499\u20132511 (2022)","journal-title":"Arab. J. Sci. Eng."},{"issue":"1","key":"1244_CR43","first-page":"289","volume":"6","author":"NA Angraini","year":"2024","unstructured":"Angraini, N.A., Lhaksmana, K.M.: Sentiment Analysis About Legislative Elections using Deep Learning with LSTM and CNN Models. Building Inf. Technol. Sci. (BITS). 6(1), 289\u2013299 (2024)","journal-title":"Building Inf. Technol. Sci. (BITS)"},{"key":"1244_CR44","doi-asserted-by":"publisher","unstructured":"He, A., Abisado, M.: Text Sentiment Analysis of Douban Film Short Comments Based on BERT-CNN-BiLSTM-Att Model, in IEEE Access, vol. 12, pp. 45229\u201345237, (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3381515","DOI":"10.1109\/ACCESS.2024.3381515"},{"issue":"2","key":"1244_CR45","doi-asserted-by":"publisher","first-page":"183","DOI":"10.22581\/muet1982.3130","volume":"43","author":"S Ahmad","year":"2024","unstructured":"Ahmad, S., Saqib, S.M., Syed, A.H.: CNN and LSTM based hybrid deep learning model for sentiment analysis on Arabic text reviews. Mehran Univ. Res. J. Eng. Technol. 43(2), 183\u2013194 (2024)","journal-title":"Mehran Univ. Res. J. Eng. Technol."},{"issue":"4","key":"1244_CR46","doi-asserted-by":"publisher","first-page":"e0231924","DOI":"10.1371\/journal.pone.0231924","volume":"15","author":"J. Gao","year":"2020","unstructured":"Gao, J., Zheng, P., Jia, Y., Chen, H., Mao, Y., Chen, S., Wang, Y., Fu, H., Dai, J.: Mental health problems and social media exposure during COVID-19 outbreak. Plos one. 15(4), e0231924 (2020)","journal-title":"Plos one"},{"issue":"3","key":"1244_CR47","doi-asserted-by":"publisher","first-page":"171","DOI":"10.3961\/jpmph.20.094","volume":"53","author":"S Tasnim","year":"2020","unstructured":"Tasnim, S., Hossain, M.M., Mazumder, H.: Impact of rumors and misinformation on COVID-19 in social media. J. Prev. Med. public. health. 53(3), 171\u2013174 (2020)","journal-title":"J. Prev. Med. public. health"},{"key":"1244_CR48","doi-asserted-by":"crossref","unstructured":"Ni, M.Y., Yang, L., Leung, C.M., Li, N., Yao, X.I., Wang, Y., Gabriel, M., Leung, B.J., Cowling, Liao, Q.: Mental health, risk factors, and social media use during the COVID-19 epidemic and cordon sanitaire among the community and health professionals in Wuhan, China: cross-sectional survey. JMIR mental health, 7(5), e19009, (2020)","DOI":"10.2196\/19009"},{"key":"1244_CR49","doi-asserted-by":"publisher","first-page":"102066","DOI":"10.1016\/j.ajp.2020.102066","volume":"52","author":"RP Rajkumar","year":"2020","unstructured":"Rajkumar, R.P.: COVID-19 and mental health: A review of the existing literature. Asian J. Psychiatry. 52, 102066 (2020)","journal-title":"Asian J. Psychiatry"},{"issue":"2\u20133","key":"1244_CR50","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1080\/19312458.2018.1455817","volume":"12","author":"E Rudkowsky","year":"2018","unstructured":"Rudkowsky, E., Haselmayer, M., Wastian, M., Jenny, M., Emrich, \u0160., Sedlmair, M.: More than bags of words: Sentiment analysis with word embeddings. Communication Methods Measures. 12(2\u20133), 140\u2013157 (2018)","journal-title":"Communication Methods Measures"},{"issue":"1","key":"1244_CR51","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1080\/09296174.2020.1767481","volume":"29","author":"R Feng","year":"2022","unstructured":"Feng, R., Yang, C., Qu, Y.: A word embedding model for analyzing patterns and their distributional semantics. J. Quant. Linguistics. 29(1), 80\u2013105 (2022)","journal-title":"J. Quant. Linguistics"},{"key":"1244_CR52","doi-asserted-by":"crossref","unstructured":"Selva Birunda, S., Kanniga Devi, R.: A review on word embedding techniques for text classification. Innovative Data Communication Technologies and Application: Proceedings of ICIDCA 2020, 267\u2013281,(2021)","DOI":"10.1007\/978-981-15-9651-3_23"},{"key":"1244_CR53","doi-asserted-by":"publisher","first-page":"41283","DOI":"10.1109\/ACCESS.2021.3064830","volume":"9","author":"S Tam","year":"2021","unstructured":"Tam, S., Said, R.B., Tanri\u00f6ver, \u00d6.\u00d6.: A ConvBiLSTM deep learning model-based approach for Twitter sentiment classification. IEEE Access. 9, 41283\u201341293 (2021)","journal-title":"IEEE Access."},{"issue":"8","key":"1244_CR54","doi-asserted-by":"publisher","first-page":"e0220976","DOI":"10.1371\/journal.pone.0220976","volume":"14","author":"B. Jang","year":"2019","unstructured":"Jang, B., Kim, I., Kim, J.W.: Word2vec convolutional neural networks for classification of news articles and tweets. PloS one. 14(8), e0220976 (2019)","journal-title":"PloS one"},{"key":"1244_CR55","doi-asserted-by":"crossref","unstructured":"Pinaya, W.H.L., Vieira, S., Garcia-Dias, R., Mechelli, A.: Convolutional neural networks. In: Machine learning, Academic,173\u2013191, (2020)","DOI":"10.1016\/B978-0-12-815739-8.00010-9"},{"key":"1244_CR56","doi-asserted-by":"publisher","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","volume":"53","author":"A Khan","year":"2020","unstructured":"Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53, 5455\u20135516 (2020)","journal-title":"Artif. Intell. Rev."},{"issue":"1","key":"1244_CR57","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1109\/TTE.2022.3197927","volume":"9","author":"Q Yao","year":"2022","unstructured":"Yao, Q., Lu, D.D.C., Lei, G.: A surface temperature estimation method for lithium-ion battery using enhanced GRU-RNN. IEEE Trans. Transp. Electrification. 9(1), 1103\u20131112 (2022)","journal-title":"IEEE Trans. Transp. Electrification"},{"key":"1244_CR58","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273\u2013297 (1995). https:\/\/doi.org\/10.1007\/BF00994018","journal-title":"Mach. Learn."},{"key":"1244_CR59","doi-asserted-by":"crossref","unstructured":"Kumar, M., Bhatia, R., Rattan, D.: A survey of Web crawlers for information retrieval. Wiley Interdisciplinary Reviews: Data Min. Knowl. Discovery, 7(6), e1218, (2017)","DOI":"10.1002\/widm.1218"},{"key":"1244_CR60","doi-asserted-by":"crossref","unstructured":"Bifet, A., Frank, E.: Sentiment knowledge discovery in twitter streaming data. In International conference on discovery science . Berlin, Heidelberg: Springer Berlin Heidelberg,1\u201315, (2010)","DOI":"10.1007\/978-3-642-16184-1_1"},{"key":"1244_CR61","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Barron, J.T., Papandreou, G., Murphy, K., Yuille, A.L.: Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform. In Proceedings of the IEEE conference on computer vision and pattern recognition pp. 4545\u20134554, (2016)","DOI":"10.1109\/CVPR.2016.492"},{"key":"1244_CR62","doi-asserted-by":"publisher","first-page":"107396","DOI":"10.1016\/j.asoc.2021.107396","volume":"108","author":"RV Karthik","year":"2021","unstructured":"Karthik, R.V., Ganapathy, S.: A fuzzy recommendation system for predicting the customers interests using sentiment analysis and ontology in e-commerce. Appl. Soft Comput. 108, 107396 (2021)","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"1244_CR63","doi-asserted-by":"publisher","first-page":"102435","DOI":"10.1016\/j.ipm.2020.102435","volume":"58","author":"RK Behera","year":"2021","unstructured":"Behera, R.K., Jena, M., Rath, S.K., Misra, S.: Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data. Inf. Process. Manag. 58(1), 102435 (2021)","journal-title":"Inf. Process. Manag."},{"key":"1244_CR64","doi-asserted-by":"publisher","first-page":"23253","DOI":"10.1109\/ACCESS.2017.2776930","volume":"6","author":"Z Jianqiang","year":"2018","unstructured":"Jianqiang, Z., Xiaolin, G., Xuejun, Z.: Deep convolution neural networks for twitter sentiment analysis. IEEE access. 6, 23253\u201323260 (2018)","journal-title":"IEEE access."},{"key":"1244_CR65","unstructured":"IMDB Dataset of 50K Movie Reviews: (2018). https:\/\/www.kaggle.com\/datasets\/lakshmi25npathi\/imdb-dataset-of-50k-movie-reviews\/data"},{"key":"1244_CR66","unstructured":"Self-Driving, C.: (2021). https:\/\/www.kaggle.com\/datasets\/alincijov\/self-driving-cars\/data"},{"key":"1244_CR67","doi-asserted-by":"crossref","unstructured":"Bisong, E., Bisong, E.: Google colaboratory. Building machine learning and deep learning models on google cloud platform: a comprehensive guide for beginners, 59\u201364, (2019)","DOI":"10.1007\/978-1-4842-4470-8_7"},{"issue":"2","key":"1244_CR68","doi-asserted-by":"publisher","first-page":"227","DOI":"10.3102\/1076998619872761","volume":"45","author":"B Pang","year":"2020","unstructured":"Pang, B., Nijkamp, E., Wu, Y.N.: Deep learning with tensorflow: A review. J. Educational Behav. Stat. 45(2), 227\u2013248 (2020)","journal-title":"J. Educational Behav. Stat."},{"key":"1244_CR69","doi-asserted-by":"crossref","unstructured":"Ketkar, N., Ketkar, N.: Introduction to keras. Deep learning with python: A hands-on Introduction, 97\u2013111,(2017)","DOI":"10.1007\/978-1-4842-2766-4_7"},{"key":"1244_CR70","unstructured":"Lutz, M.: Programming python. O\u2019Reilly Media, Inc. (2001)"},{"issue":"1","key":"1244_CR71","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s13677-022-00386-3","volume":"12","author":"MS Ba\u015farslan","year":"2023","unstructured":"Ba\u015farslan, M.S., Kayaalp, F.: MBi-GRUMCONV: A novel Multi Bi-GRU and Multi CNN-Based deep learning model for social media sentiment analysis. J. Cloud Comput. 12(1), 5 (2023)","journal-title":"J. Cloud Comput."},{"key":"1244_CR72","first-page":"55","volume-title":"Neural Networks: Tricks of the trade","author":"L Prechelt","year":"2002","unstructured":"Prechelt, L.: Early stopping-but when? In: Neural Networks: Tricks of the trade, Springer Berlin Heidelberg, Berlin, Heidelberg , 55\u201369,(2002)"},{"issue":"6","key":"1244_CR73","first-page":"1467","volume":"8","author":"Y Li","year":"2021","unstructured":"Li, Y., Zhang, H., Liu, X., Wang, S.: A scalable multichannel sentiment analysis model with enhanced semantic understanding and redundancy reduction. IEEE Trans. Comput. Social Syst. 8(6), 1467\u20131478 (2021)","journal-title":"IEEE Trans. Comput. Social Syst."},{"issue":"2","key":"1244_CR74","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/s13042-020-01175-7","volume":"12","author":"D Tang","year":"2021","unstructured":"Tang, D., Qin, B., Liu, T.: Cross-domain sentiment-aware word embeddings for review sentiment analysis. Int. J. Mach. Learn. Cybernet. 12(2), 343\u2013354 (2021)","journal-title":"Int. J. Mach. Learn. Cybernet."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-026-01244-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01244-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01244-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T12:03:06Z","timestamp":1773230586000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-026-01244-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,2]]},"references-count":74,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1244"],"URL":"https:\/\/doi.org\/10.1007\/s44196-026-01244-9","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,2]]},"assertion":[{"value":"27 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human participants and\/or animals"}},{"value":"No funding is available for doing this research work. The authors declare that they have no conflict of interest. The authors declare no potential conflicts of interest (financial or non-financial).","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors gave full permission to publish the research work.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}}],"article-number":"114"}}