{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T00:06:55Z","timestamp":1726445215203},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T00:00:00Z","timestamp":1640822400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T00:00:00Z","timestamp":1640822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s42979-021-00993-y","type":"journal-article","created":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T12:02:28Z","timestamp":1640865748000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Categorizing Sentiment Polarities in Social Networks Data Using Convolutional Neural Network"],"prefix":"10.1007","volume":"3","author":[{"given":"Gaurav","family":"Meena","sequence":"first","affiliation":[]},{"given":"Krishna Kumar","family":"Mohbey","sequence":"additional","affiliation":[]},{"given":"Ajay","family":"Indian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,30]]},"reference":[{"issue":"9","key":"993_CR1","doi-asserted-by":"publisher","first-page":"1910","DOI":"10.1109\/TMM.2016.2575738","volume":"18","author":"X Lei","year":"2016","unstructured":"Lei X, Qian X, Zhao G. Rating prediction based on social sentiment from textual reviews. IEEE Trans Multimed. 2016;18(9):1910\u201321.","journal-title":"IEEE Trans. Multimed."},{"key":"993_CR2","doi-asserted-by":"crossref","unstructured":"Cambria E et al. SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. Proceedings of the AAAI conference on artificial intelligence. 2018; 32(1)","DOI":"10.1609\/aaai.v32i1.11559"},{"issue":"4","key":"993_CR3","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1016\/j.asej.2014.04.011","volume":"5","author":"W Medhat","year":"2014","unstructured":"Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J. 2014;5(4):1093\u2013113.","journal-title":"Ain Shams Eng J"},{"issue":"1","key":"993_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00416ED1V01Y201204HLT016","volume":"5","author":"B Liu","year":"2012","unstructured":"Liu B. Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol. 2012;5(1):1\u2013167.","journal-title":"Synth. Lect. Hum Lang Technol"},{"key":"993_CR5","doi-asserted-by":"crossref","unstructured":"Suresh Kumar S et al. A review on wearable and contactless sensing for COVID-19 with policy challenges. Front. Commun. Netw. (2021)","DOI":"10.3389\/frcmn.2021.636293"},{"issue":"5","key":"993_CR6","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1007\/s00521-018-3466-5","volume":"31","author":"S Lokesh","year":"2019","unstructured":"Lokesh S, et al. An automatic tamil speech recognition system by using bidirectional recurrent neural network with self-organizing map. Neural Comput Appl. 2019;31(5):1521\u201331.","journal-title":"Neural Comput. Appl."},{"issue":"9","key":"993_CR7","doi-asserted-by":"publisher","first-page":"2653","DOI":"10.3390\/s20092653","volume":"20","author":"W Taylor","year":"2020","unstructured":"Taylor W, et al. An intelligent non-invasive real-time human activity recognition system for next-generation healthcare. Sensors. 2020;20(9):2653.","journal-title":"Sensors"},{"key":"993_CR8","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.inffus.2017.12.006","volume":"44","author":"I Chaturvedi","year":"2018","unstructured":"Chaturvedi I, et al. Distinguishing between facts and opinions for sentiment analysis: survey and challenges. Inf Fusion. 2018;44:65\u201377.","journal-title":"Inf Fusion"},{"key":"993_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-55394-8","volume-title":"Affective computing and sentiment analysis. A practical guide to sentiment analysis","author":"E Cambria","year":"2017","unstructured":"Cambria E, et al. Affective computing and sentiment analysis. A practical guide to sentiment analysis. Cham: Springer; 2017. p. 1\u201310."},{"issue":"10","key":"993_CR10","doi-asserted-by":"publisher","first-page":"e4746","DOI":"10.1002\/cpe.4746","volume":"31","author":"X-Q Liu","year":"2019","unstructured":"Liu X-Q, Qiu-Lin W, Pan W-T. Sentiment classification of micro-blog comments based on Randomforest algorithm. Concurr Comput Pract Exp. 2019;31(10):e4746.","journal-title":"Concurr Comput Pract Exp"},{"key":"993_CR11","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, et al. Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst Appl. 2017;77:236\u201346.","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"993_CR12","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1007\/s10618-011-0238-6","volume":"24","author":"M Tsytsarau","year":"2012","unstructured":"Tsytsarau M, Palpanas T. Survey on mining subjective data on the web. Data Min Knowl Discov. 2012;24(3):478\u2013514.","journal-title":"Data Min Knowl Discov."},{"key":"993_CR13","doi-asserted-by":"crossref","unstructured":"Chen PJ, Ding JJ, Hsu HW, Wang CY, Wang JC. Improved convolutional neural network based scene classification using long short-term memory and label relations. In 2017 IEEE international conference on multimedia and expo workshops (ICMEW), IEEE 2017; pp. 429-434","DOI":"10.1109\/ICMEW.2017.8026239"},{"key":"993_CR14","volume-title":"A convolutional attention model for text classification. National CCF conference on natural language processing and Chinese computing","author":"J Du","year":"2017","unstructured":"Du J, et al. A convolutional attention model for text classification. National CCF conference on natural language processing and Chinese computing. Cham: Springer; 2017."},{"key":"993_CR15","unstructured":"Pitsilis GK, Heri R, Helge L. Detecting offensive language in tweets using deep learning. arXiv preprint arXiv:1801.04433 (2018)"},{"key":"993_CR16","doi-asserted-by":"crossref","unstructured":"Hassan A, Ausif M. Deep learning approach for sentiment analysis of short texts. 2017 3rd international conference on control, automation and robotics (ICCAR). IEEE, (2017)","DOI":"10.1109\/ICCAR.2017.7942788"},{"key":"993_CR17","doi-asserted-by":"crossref","unstructured":"Shen Q, Zijian W, Yaoru S. Sentiment analysis of movie reviews based on cnn-blstm. International conference on intelligence science. Springer, Cham (2017)","DOI":"10.1007\/978-3-319-68121-4_17"},{"issue":"11","key":"993_CR18","doi-asserted-by":"publisher","first-page":"8187","DOI":"10.1007\/s00500-019-04402-8","volume":"24","author":"L-C Chen","year":"2020","unstructured":"Chen L-C, Lee C-M, Chen M-Y. Exploration of social media for sentiment analysis using deep learning. Soft Comput. 2020;24(11):8187\u201397.","journal-title":"Soft Comput"},{"key":"993_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-021-08580-3","author":"UD Gandhi","year":"2021","unstructured":"Gandhi UD, et al. Sentiment analysis on twitter data by using convolutional neural network (CNN) and long short term memory (LSTM). Wirel Pers Commun. 2021. https:\/\/doi.org\/10.1007\/s11277-021-08580-3.","journal-title":"Wirel Pers Commun"},{"key":"993_CR20","unstructured":"O\u2019Reilly T, John B. Opening welcome: state of the internet industry. San Francisco, California, (2004)"},{"key":"993_CR21","doi-asserted-by":"publisher","unstructured":"Garrigos-Simon FJ, Rafael LA, Teresa BR. Social networks and Web 3.0: their impact on the management and marketing of organizations. Manag Decis. Doi: https:\/\/doi.org\/10.1108\/00251741211279657(2012)","DOI":"10.1108\/00251741211279657"},{"key":"993_CR22","unstructured":"Del Vigna12 F et al. Hate me, hate me not: hate speech detection on facebook. Proceedings of the first italian conference on cybersecurity (ITASEC17) (2017)"},{"issue":"3","key":"993_CR23","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273\u201397.","journal-title":"Mach Learn"},{"issue":"2","key":"993_CR24","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1111\/j.2517-6161.1958.tb00292.x","volume":"20","author":"DR Cox","year":"1958","unstructured":"Cox DR. The regression analysis of binary sequences. J R Stat Soc Ser B (Methodol). 1958;20(2):215\u201332.","journal-title":"J R Stat Soc Ser B (Methodol)"},{"issue":"2","key":"993_CR25","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L. Bagging predictors. Mach Learn. 1996;24(2):123\u201340.","journal-title":"Mach Learn"},{"issue":"3","key":"993_CR26","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1109\/21.97458","volume":"21","author":"SR Safavian","year":"1991","unstructured":"Safavian SR, David L. A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern. 1991;21(3):660\u201374.","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"993_CR27","doi-asserted-by":"publisher","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky A. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys D Nonlinear Phenom. 2020;404:132306.","journal-title":"Phys D Nonlinear Phenom"},{"key":"993_CR28","doi-asserted-by":"crossref","unstructured":"Albawi S, Tareq AM, Saad A-Z. Understanding of a convolutional neural network. 2017 International Conference on engineering and technology (ICET). IEEE (2017)","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"issue":"3","key":"993_CR29","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1007\/s11277-016-3346-1","volume":"89","author":"K Lyu","year":"2016","unstructured":"Lyu K, Kim H. Sentiment analysis using word polarity of social media. Wirel Pers Commun. 2016;89(3):941\u201358.","journal-title":"Wirel Pers Commun"},{"issue":"2","key":"993_CR30","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1007\/s11277-017-5235-7","volume":"102","author":"G Wang","year":"2018","unstructured":"Wang G, Pengbo P, Liang Y. Topic and sentiment words extraction in cross-domain product reviews. Wirel Pers Commun. 2018;102(2):1773\u201383.","journal-title":"Wirel Pers Commun"},{"issue":"5","key":"993_CR31","doi-asserted-by":"publisher","first-page":"102221","DOI":"10.1016\/j.ipm.2020.102221","volume":"57","author":"X Feng","year":"2020","unstructured":"Feng X, Zhenchun P, Rui X. E-commerce product review sentiment classification based on a na\u00efve Bayes continuous learning framework. Inf Process Manag. 2020;57(5):102221.","journal-title":"Inf Process Manag"},{"issue":"1","key":"993_CR32","doi-asserted-by":"publisher","first-page":"102121","DOI":"10.1016\/j.ipm.2019.102121","volume":"57","author":"A Elnagar","year":"2020","unstructured":"Elnagar A, Al-Debsi R, Einea O. Arabic text classification using deep learning models. Inf Process Manag. 2020;57(1):102121.","journal-title":"Inf Process Manag"},{"key":"993_CR33","doi-asserted-by":"publisher","first-page":"6861","DOI":"10.1109\/ACCESS.2019.2963426","volume":"8","author":"S Seo","year":"2020","unstructured":"Seo S, et al. Comparative study of deep learning-based sentiment classification. IEEE Access. 2020;8:6861\u201375.","journal-title":"IEEE Access"},{"key":"993_CR34","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.ijinfomgt.2017.12.002","volume":"39","author":"S Stieglitz","year":"2018","unstructured":"Stieglitz S, et al. Social media analytics-Challenges in topic discovery, data collection, and data preparation. Int J Inf Manag. 2018;39:156\u201368.","journal-title":"Int J Inf Manag"},{"issue":"11","key":"993_CR35","doi-asserted-by":"publisher","first-page":"2347","DOI":"10.3390\/app9112347","volume":"9","author":"H Kim","year":"2019","unstructured":"Kim H, Jeong Y-S. Sentiment classification using convolutional neural networks. Appl Sci. 2019;9(11):2347.","journal-title":"Appl Sci"},{"key":"993_CR36","unstructured":"Spencer J, Gulden U. Sentimentor: sentiment analysis of twitter data. SDAD@ ECML\/PKDD (2012)"},{"issue":"10","key":"993_CR37","first-page":"765","volume":"12","author":"A Driyani","year":"2021","unstructured":"Driyani A. Twitter sentiment analysis of mobile reviews using kernelized SVM. Turk J Comput Math Educ (TURCOMAT). 2021;12(10):765\u20138.","journal-title":"Turk J Comput Math Educ (TURCOMAT)"},{"key":"993_CR38","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/978-981-13-8759-3_12","volume-title":"Advertisement prediction in social media environment using big data framework. Multimedia big data computing for IoT applications","author":"KK Mohbey","year":"2020","unstructured":"Mohbey KK, Sunil K, Vartika K. Advertisement prediction in social media environment using big data framework. Multimedia big data computing for IoT applications. Singapore: Springer; 2020. p. 323\u201341."},{"key":"993_CR39","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.procs.2020.06.038","volume":"173","author":"A Sharma","year":"2020","unstructured":"Sharma A, Ghose U. Sentimental analysis of twitter data with respect to general elections in India. Proced Comput Sci. 2020;173:325\u201334.","journal-title":"Proced Comput Sci"},{"key":"993_CR40","unstructured":"Al-mashhadani MI, Kilan MH, Enas TK. Sentiment analysis using optimized feature sets in different facebook\/twitter dataset domains using big data. Iraqi J Comput Sci Math 3.1 (2022)"},{"key":"993_CR41","doi-asserted-by":"crossref","unstructured":"Bagheri H, Md Johirul I. Sentiment analysis of twitter data. arXiv preprint arXiv:1711.10377 (2017)","DOI":"10.31219\/osf.io\/6xc4y"},{"volume-title":"Mining text data","year":"2012","key":"993_CR42","unstructured":"Aggarwal CC, Cheng XZ, editors. Mining text data. Berlin: Springer; 2012."},{"key":"993_CR43","doi-asserted-by":"crossref","unstructured":"Hu M, Bing L. Mining and summarizing customer reviews. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (2004)","DOI":"10.1145\/1014052.1014073"},{"key":"993_CR44","doi-asserted-by":"crossref","unstructured":"Whitelaw C, Navendu G, Shlomo A. Using appraisal groups for sentiment analysis. Proceedings of the 14th ACM international conference on information and knowledge management (2005)","DOI":"10.1145\/1099554.1099714"},{"key":"993_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2015.07.007","volume":"71","author":"H Wu","year":"2015","unstructured":"Wu H, Xiaodong G. Towards dropout training for convolutional neural networks. Neural Netw. 2015;71:1\u201310.","journal-title":"Neural Netw"},{"key":"993_CR46","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA. 2016; 30:2818\u201326","DOI":"10.1109\/CVPR.2016.308"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-021-00993-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-021-00993-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-021-00993-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T10:57:00Z","timestamp":1726397820000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-021-00993-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,30]]},"references-count":46,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["993"],"URL":"https:\/\/doi.org\/10.1007\/s42979-021-00993-y","relation":{},"ISSN":["2662-995X","2661-8907"],"issn-type":[{"type":"print","value":"2662-995X"},{"type":"electronic","value":"2661-8907"}],"subject":[],"published":{"date-parts":[[2021,12,30]]},"assertion":[{"value":"1 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors of this manuscript state that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"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":"Ethical approval"}}],"article-number":"116"}}