{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T06:06:34Z","timestamp":1777183594068,"version":"3.51.4"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T00:00:00Z","timestamp":1704153600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T00:00:00Z","timestamp":1704153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Manipal Academy of Higher Education, Bangalore"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>With more and more teaching learning activities being shifted to online mode, the education system has seen a drastic paradigm shift in the recent times. Learner opinion has emerged as an important metric for gaining valuable insights about teaching\u2013learning process, student satisfaction, course popularity, etc. Traditional methods for opinion mining of learner feedback are tedious and require manual intervention. The author, in this work has proposed a hybrid bio-inspired metaheuristic feature selection approach for opinion mining of learner comments regarding a course. Experimental work is conducted over a real-world education dataset comprising of 110\u00a0K learner comments (referred to as Educational Dataset now onwards) collected from Coursera and learner data from academic institution MSIT. Based on the experimental results over the collected dataset, the proposed model achieves an accuracy of 92.24%. Further, for comparative analysis, results of the proposed model are compared with the ENN models for different embeddings, viz., Word2Vec, tf-idf and domain-specific embedding for the SemEval-14 Task 4. The hybrid bio-inspired metaheuristic model outperforms the pre-existing models for the standard dataset too.<\/jats:p>","DOI":"10.1007\/s42979-023-02526-1","type":"journal-article","created":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T11:02:36Z","timestamp":1704193356000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Hybrid Bio-inspired Fuzzy Feature Selection Approach for Opinion Mining of Learner Comments"],"prefix":"10.1007","volume":"5","author":[{"given":"Divya","family":"Jatain","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Niranjanamurthy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8445-3469","authenticated-orcid":false,"given":"P.","family":"Dayananda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,2]]},"reference":[{"key":"2526_CR1","doi-asserted-by":"publisher","first-page":"422","DOI":"10.21123\/BSJ.2022.19.2.0422","volume":"19","author":"NF Al-Bakri","year":"2022","unstructured":"Al-Bakri NF, Yonan JF, Sadiq AT, Abid AS. Tourism companies assessment via social media using sentiment analysis. Baghdad Sci J. 2022;19:422\u20139. https:\/\/doi.org\/10.21123\/BSJ.2022.19.2.0422.","journal-title":"Baghdad Sci J"},{"key":"2526_CR2","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/j.procs.2019.12.002","volume":"162","author":"MM Ag\u00fcero-Torales","year":"2019","unstructured":"Ag\u00fcero-Torales MM, Cobo MJ, Herrera-Viedma E, L\u00f3pez-Herrera AG. A cloud-based tool for sentiment analysis in reviews about restaurants on TripAdvisor. Procedia Comput Sci. 2019;162:392\u20139. https:\/\/doi.org\/10.1016\/j.procs.2019.12.002.","journal-title":"Procedia Comput Sci"},{"key":"2526_CR3","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/MIS.2016.31","volume":"31","author":"E Cambria","year":"2016","unstructured":"Cambria E. Affective computing and sentiment analysis. IEEE Intell Syst. 2016;31:102\u20137. https:\/\/doi.org\/10.1109\/MIS.2016.31.","journal-title":"IEEE Intell Syst"},{"key":"2526_CR4","doi-asserted-by":"publisher","DOI":"10.1108\/OIR-08-2015-0289","author":"AM Abirami","year":"2017","unstructured":"Abirami AM, Aa A. Sentiment analysis model to emphasize the impact of online reviews in healthcare industry. Online Inform Rev. 2017. https:\/\/doi.org\/10.1108\/OIR-08-2015-0289.","journal-title":"Online Inform Rev"},{"key":"2526_CR5","doi-asserted-by":"publisher","unstructured":"Paul MJ, Sarker A, Brownstein JS, Nikfarjam A, Scotch M, Smith KL, Gonzalez G. Social media mining for public health monitoring and surveillance. Pacific Symposium on Biocomputing 2016; 468\u2013479, https:\/\/doi.org\/10.1142\/9789814749411_0043.","DOI":"10.1142\/9789814749411_0043"},{"key":"2526_CR6","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.32604\/cmc.2022.018131","volume":"70","author":"AF Ibrahim","year":"2022","unstructured":"Ibrahim AF, Hassaballah M, Ali AA, Nam Y, Ibrahim IA. COVID19 outbreak: a hierarchical framework for user sentiment analysis. Comput Mater Continua. 2022;70:2507\u201324. https:\/\/doi.org\/10.32604\/cmc.2022.018131.","journal-title":"Comput Mater Continua"},{"key":"2526_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/electronics10101133","volume":"10","author":"Z Kastrati","year":"2021","unstructured":"Kastrati Z, Ahmedi L, Kurti A, Kadriu F, Murtezaj D, Gashi F. A deep learning sentiment analyser for social media comments in low-resource languages. Electronics (Switzerland). 2021;10:1\u201319. https:\/\/doi.org\/10.3390\/electronics10101133.","journal-title":"Electronics (Switzerland)"},{"key":"2526_CR8","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1080\/19331681.2015.1132401","volume":"13","author":"A Jungherr","year":"2016","unstructured":"Jungherr A. Twitter use in election campaigns: a systematic literature review. J Inform Technol Polit. 2016;13:72\u201391. https:\/\/doi.org\/10.1080\/19331681.2015.1132401.","journal-title":"J Inform Technol Polit"},{"key":"2526_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113540","volume":"158","author":"J Duan","year":"2020","unstructured":"Duan J, Luo B, Zeng J. Semi-supervised learning with generative model for sentiment classification of stock messages. Expert Syst Appl. 2020;158: 113540. https:\/\/doi.org\/10.1016\/j.eswa.2020.113540.","journal-title":"Expert Syst Appl"},{"key":"2526_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115470","volume":"184","author":"AE de Oliveira-Carosia","year":"2021","unstructured":"de Oliveira-Carosia AE, Coelho GP, da Silva AEA. Investment strategies applied to the Brazilian stock market: a methodology based on sentiment analysis with deep learning. Expert Syst Appl. 2021;184: 115470. https:\/\/doi.org\/10.1016\/j.eswa.2021.115470.","journal-title":"Expert Syst Appl"},{"key":"2526_CR11","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1007\/s12559-017-9503-3","volume":"9","author":"MZ Asghar","year":"2017","unstructured":"Asghar MZ, Khan A, Bibi A, Kundi FM, Ahmad H. Sentence-level emotion detection framework using rule-based classification. Cogn Comput. 2017;9:868\u201394. https:\/\/doi.org\/10.1007\/s12559-017-9503-3.","journal-title":"Cogn Comput"},{"key":"2526_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114155","volume":"167","author":"AH Alamoodi","year":"2021","unstructured":"Alamoodi AH, Zaidan BB, Zaidan AA, Albahri OS, Mohammed KI, Malik RQ, Almahdi EM, Chyad MA, Tareq Z, Albahri AS, et al. Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: a systematic review. Expert Syst Appl. 2021;167: 114155. https:\/\/doi.org\/10.1016\/j.eswa.2020.114155.","journal-title":"Expert Syst Appl"},{"key":"2526_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114604","volume":"171","author":"Y Han","year":"2021","unstructured":"Han Y, Moghaddam M. Analysis of sentiment expressions for user-centered design. Expert Syst Appl. 2021;171: 114604. https:\/\/doi.org\/10.1016\/j.eswa.2021.114604.","journal-title":"Expert Syst Appl"},{"key":"2526_CR14","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s10796-010-9275-8","volume":"13","author":"O Oh","year":"2011","unstructured":"Oh O, Agrawal M, Rao HR. Information control and terrorism: tracking the mumbai terrorist attack through Twitter. Inf Syst Front. 2011;13:33\u201343. https:\/\/doi.org\/10.1007\/s10796-010-9275-8.","journal-title":"Inf Syst Front"},{"key":"2526_CR15","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1111\/j.1464-0597.1992.tb00712.x","volume":"41","author":"R Pekrun","year":"2008","unstructured":"Pekrun R. The impact of emotions on learning and achievement: towards a theory of cognitive\/motivational mediators. Appl Psychol. 2008;41:359\u201376. https:\/\/doi.org\/10.1111\/j.1464-0597.1992.tb00712.x.","journal-title":"Appl Psychol"},{"key":"2526_CR16","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1111\/j.1751-228X.2007.00004.x","volume":"1","author":"M Immordino-Yang","year":"2007","unstructured":"Immordino-Yang M, Damasio A. We feel, therefore we learn: the relevance of affective and social neuroscience to education. Mind Brain Educ. 2007;1:3\u201310. https:\/\/doi.org\/10.1111\/j.1751-228X.2007.00004.x.","journal-title":"Mind Brain Educ"},{"key":"2526_CR17","doi-asserted-by":"crossref","unstructured":"Kerkeni L, Serrestou Y, Mbarki M, Raoof K, Mahjoub M. A review on speech emotion recognition: case of pedagogical interaction in classroom. 2017; pp 1\u20137.","DOI":"10.1109\/ATSIP.2017.8075575"},{"key":"2526_CR18","unstructured":"Reilly R. The science behind the art of teaching science: emotional state and learning. 2004."},{"key":"2526_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100377","volume":"40","author":"C Salazar","year":"2021","unstructured":"Salazar C, Aguilar J, Monsalve-Pulido J, Montoya E. Affective recommender systems in the educational field. A systematic literature review. Comput Sci Rev. 2021;40: 100377. https:\/\/doi.org\/10.1016\/j.cosrev.2021.100377.","journal-title":"Comput Sci Rev"},{"key":"2526_CR20","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/s00403-020-02088-9","volume":"313","author":"SL Schneider","year":"2021","unstructured":"Schneider SL, Council ML. Distance learning in the era of COVID-19. Arch Dermatol Res. 2021;313:389\u201390. https:\/\/doi.org\/10.1007\/s00403-020-02088-9.","journal-title":"Arch Dermatol Res"},{"key":"2526_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2019.103724","volume":"145","author":"K Hew","year":"2019","unstructured":"Hew K, Hu X, Qiao C, Tang Y. What predicts student satisfaction with MOOCs: a gradient boosting trees supervised machine learning and sentiment analysis approach. Comput Educ. 2019;145: 103724. https:\/\/doi.org\/10.1016\/j.compedu.2019.103724.","journal-title":"Comput Educ"},{"key":"2526_CR22","doi-asserted-by":"crossref","unstructured":"Altrabsheh N, Haig E, Fallahkhair S, Dhou K. Evaluation of the SA-E system for analysis of student real-time feedback. In: 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT) 2017; 60\u201361.","DOI":"10.1109\/ICALT.2017.57"},{"key":"2526_CR23","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1016\/j.chb.2013.05.024","volume":"31","author":"A Ortigosa","year":"2014","unstructured":"Ortigosa A, Mart\u00edn JM, Carro RM. Sentiment analysis in Facebook and its application to e-learning. Comput Hum Behav. 2014;31:527\u201341. https:\/\/doi.org\/10.1016\/j.chb.2013.05.024.","journal-title":"Comput Hum Behav"},{"key":"2526_CR24","doi-asserted-by":"publisher","unstructured":"Pong-inwong C, Rungworawut W. Teaching senti-lexicon for automated sentiment polarity definition in teaching evaluation. In: Proceedings - 2014 10th International Conference on Semantics, Knowledge and Grids, SKG 2014 2014, 84\u201391, https:\/\/doi.org\/10.1109\/SKG.2014.25.","DOI":"10.1109\/SKG.2014.25"},{"key":"2526_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/2385429","volume":"2016","author":"Q Rajput","year":"2016","unstructured":"Rajput Q, Haider S, Ghani S. Lexicon-based sentiment analysis of teachers\u2019 evaluation. Appl Comput Intell Soft Comput. 2016;2016:1\u201312. https:\/\/doi.org\/10.1155\/2016\/2385429.","journal-title":"Appl Comput Intell Soft Comput"},{"key":"2526_CR26","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2928872","author":"I Sindhu","year":"2019","unstructured":"Sindhu I, Daudpota S, Badar K, Bakhtyar M, Baber J, Nurunnabi M. Aspect based opinion mining on student\u2019s feedback for faculty teaching performance evaluation. IEEE Access. 2019. https:\/\/doi.org\/10.1109\/ACCESS.2019.2928872.","journal-title":"IEEE Access"},{"key":"2526_CR27","doi-asserted-by":"publisher","first-page":"106799","DOI":"10.1109\/ACCESS.2020.3000739","volume":"8","author":"Z Kastrati","year":"2020","unstructured":"Kastrati Z, Imran AS, Kurti A. Weakly supervised framework for aspect-based sentiment analysis on students\u2019 reviews of MOOCs. IEEE Access. 2020;8:106799\u2013810. https:\/\/doi.org\/10.1109\/ACCESS.2020.3000739.","journal-title":"IEEE Access"},{"key":"2526_CR28","unstructured":"Ramos J. Using TF-IDF to determine word relevance in document queries. 2003."},{"key":"2526_CR29","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems 2013, 26."},{"key":"2526_CR30","doi-asserted-by":"publisher","DOI":"10.1186\/s41239-019-0144-3","author":"E Rabin","year":"2019","unstructured":"Rabin E, Kalman Y, Kalz M. An empirical investigation of the antecedents of learner-centered outcome measures in MOOCs. Int J Educ Technol Higher Educ. 2019. https:\/\/doi.org\/10.1186\/s41239-019-0144-3.","journal-title":"Int J Educ Technol Higher Educ"},{"key":"2526_CR31","doi-asserted-by":"publisher","DOI":"10.2307\/1176821","author":"M Magolda","year":"1993","unstructured":"Magolda M, Astin A. What matters in college: four critical years revisited. Educ Res. 1993. https:\/\/doi.org\/10.2307\/1176821.","journal-title":"Educ Res"},{"key":"2526_CR32","unstructured":"Kuo Y. DigitalCommons @ USU interaction, internet self-efficacy , and self-regulated learning as predictors of student satisfaction in distance education courses. 2010."},{"key":"2526_CR33","first-page":"61","volume":"3","author":"D Bolliger","year":"2004","unstructured":"Bolliger D, Martindale T. Key factors for determining student satisfaction in online courses. Int J E-Learn. 2004;3:61\u20137.","journal-title":"Int J E-Learn"},{"key":"2526_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2018.11.001","author":"S Kumar","year":"2018","unstructured":"Kumar S, Yadava M, Roy P. Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction. Inform Fus. 2018. https:\/\/doi.org\/10.1016\/j.inffus.2018.11.001.","journal-title":"Inform Fus"},{"key":"2526_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102656","volume":"58","author":"H Zhao","year":"2021","unstructured":"Zhao H, Liu Z, Yao X, Yang Q. A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach. Inf Process Manag. 2021;58: 102656. https:\/\/doi.org\/10.1016\/j.ipm.2021.102656.","journal-title":"Inf Process Manag"},{"key":"2526_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105236","author":"J Bernabe-Moreno","year":"2019","unstructured":"Bernabe-Moreno J, Tejeda-Lorente A, Herce-Zelaya J, Porcel C, Herrera-Viedma E. A context-aware embeddings supported method to extract a fuzzy sentiment polarity dictionary. Knowl-Based Syst. 2019. https:\/\/doi.org\/10.1016\/j.knosys.2019.105236.","journal-title":"Knowl-Based Syst"},{"key":"2526_CR37","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.neucom.2015.09.134","volume":"210","author":"H Li","year":"2016","unstructured":"Li H, Cui J, Shen B, Ma J. An intelligent movie recommendation system through group-level sentiment analysis in microblogs. Neurocomput. 2016;210:164\u201373. https:\/\/doi.org\/10.1016\/j.neucom.2015.09.134.","journal-title":"Neurocomput"},{"key":"2526_CR38","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-3-030-06149-4_8","volume-title":"Current trends in semantic web technologies: theory and practice","author":"FJ Ram\u00edrez-Tinoco","year":"2019","unstructured":"Ram\u00edrez-Tinoco FJ, Alor-Hern\u00e1ndez G, S\u00e1nchez-Cervantes JL, del Salas-Z\u00e1rate M. Use of sentiment analysis techniques in healthcare domain. In: Alor-Hern\u00e1ndez G, S\u00e1nchez-Cervantes JL, Rodr\u00edguez-Gonz\u00e1lez A, Valencia-Garc\u00eda R, editors. Current trends in semantic web technologies: theory and practice. Cham: Springer International Publishing; 2019. p. 189\u2013212."},{"key":"2526_CR39","doi-asserted-by":"crossref","unstructured":"Al-Asadi M, Tasdemir S. Using artificial intelligence against the phenomenon of fake news: a systematic literature review. 2022; pp. 39\u201354 ISBN 978-3-030-90086-1.","DOI":"10.1007\/978-3-030-90087-8_2"},{"key":"2526_CR40","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.dss.2010.12.006","volume":"51","author":"S Marston","year":"2011","unstructured":"Marston S, Li Z, Bandyopadhyay S, Zhang J, Ghalsasi A. Cloud computing\u2014the business perspective. Decis Support Syst. 2011;51:176\u201389. https:\/\/doi.org\/10.1016\/j.dss.2010.12.006.","journal-title":"Decis Support Syst"},{"key":"2526_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2019.10.014","volume":"51","author":"J Frizzo-Barker","year":"2020","unstructured":"Frizzo-Barker J, Chow-White PA, Adams PR, Mentanko J, Ha D, Green SE. Blockchain as a disruptive technology for business: a systematic review. Int J Inf Manag. 2020;51: 102029.","journal-title":"Int J Inf Manag"},{"key":"2526_CR42","doi-asserted-by":"crossref","unstructured":"Birjali M, Hssane AB, Erritali M. Learning with big data technology: the future of education. In: Proceedings of the AECIA; 2016.","DOI":"10.1007\/978-3-319-60834-1_22"},{"key":"2526_CR43","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1016\/j.ijinfomgt.2016.07.009","volume":"36","author":"I Yaqoob","year":"2016","unstructured":"Yaqoob I, Abaker I, Hashem T, Gani A, Mokhtar S, Ahmed E, Badrul N, Vasilakos A. Big data: from beginning to future. Int J Inf Manag. 2016;36:1231\u201347. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2016.07.009.","journal-title":"Int J Inf Manag"},{"key":"2526_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106435","volume":"94","author":"W Li","year":"2020","unstructured":"Li W, Zhu L, Shi Y, Guo K, Cambria E. User reviews: sentiment analysis using lexicon integrated two-channel CNN\u2013LSTM\u200b family models. Appl Soft Comput. 2020;94: 106435. https:\/\/doi.org\/10.1016\/j.asoc.2020.106435.","journal-title":"Appl Soft Comput"},{"key":"2526_CR45","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.future.2020.08.005","volume":"115","author":"ME Basiri","year":"2021","unstructured":"Basiri ME, Nemati S, Abdar M, Cambria E, Acharya UR. ABCDM: an attention-based bidirectional CNN-RNN deep model for sentiment analysis. Futur Gener Comput Syst. 2021;115:279\u201394. https:\/\/doi.org\/10.1016\/j.future.2020.08.005.","journal-title":"Futur Gener Comput Syst"},{"key":"2526_CR46","doi-asserted-by":"publisher","first-page":"23522","DOI":"10.1109\/ACCESS.2020.2969854","volume":"8","author":"L Yang","year":"2020","unstructured":"Yang L, Li Y, Wang J, Sherratt RS. Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE Access. 2020;8:23522\u201330. https:\/\/doi.org\/10.1109\/ACCESS.2020.2969854.","journal-title":"IEEE Access"},{"key":"2526_CR47","doi-asserted-by":"publisher","first-page":"149266","DOI":"10.1109\/ACCESS.2021.3124931","volume":"9","author":"MA Al-Asadi","year":"2021","unstructured":"Al-Asadi MA, Tasdemir S. Empirical comparisons for combining balancing and feature selection strategies for characterizing football players using FIFA video game system. IEEE Access. 2021;9:149266\u201386. https:\/\/doi.org\/10.1109\/ACCESS.2021.3124931.","journal-title":"IEEE Access"},{"key":"2526_CR48","doi-asserted-by":"publisher","first-page":"22631","DOI":"10.1109\/ACCESS.2022.3154767","volume":"10","author":"MA Al-Asadi","year":"2022","unstructured":"Al-Asadi MA, Tasdemir S. Predict the value of football players using FIFA video game data and machine learning techniques. IEEE Access. 2022;10:22631\u201345. https:\/\/doi.org\/10.1109\/ACCESS.2022.3154767.","journal-title":"IEEE Access"},{"key":"2526_CR49","doi-asserted-by":"crossref","unstructured":"Clarizia F, Colace F, De Santo M, Lombardi M, Pascale F, Pietrosanto A. E-Learning and Sentiment Analysis: A Case Study. In: Proceedings of the Proceedings of the 6th International Conference on Information and Education Technology; Association for Computing Machinery: New York, NY, USA, 2018; pp. 111\u2013118.","DOI":"10.1145\/3178158.3178181"},{"key":"2526_CR50","doi-asserted-by":"publisher","first-page":"2584","DOI":"10.1016\/j.eswa.2011.08.113","volume":"39","author":"CK Leong","year":"2012","unstructured":"Leong CK, Lee YH, Mak WK. Mining sentiments in SMS texts for teaching evaluation. Expert Syst Appl. 2012;39:2584\u20139. https:\/\/doi.org\/10.1016\/j.eswa.2011.08.113.","journal-title":"Expert Syst Appl"},{"key":"2526_CR51","unstructured":"To\u00e7ouglu MA, Onan A. Sentiment analysis on students\u2019 evaluation of higher educational institutions. In: Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Cebi S, Tolga AC (Eds) Proceedings of the intelligent and fuzzy techniques: smart and innovative solutions. Springer International Publishing, Cham, 2021; pp. 1693\u20131700."},{"key":"2526_CR52","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/MC.2017.133","volume":"50","author":"S Rani","year":"2017","unstructured":"Rani S, Kumar P. A sentiment analysis system to improve teaching and learning. Computer. 2017;50:36\u201343. https:\/\/doi.org\/10.1109\/MC.2017.133.","journal-title":"Computer"},{"key":"2526_CR53","doi-asserted-by":"crossref","unstructured":"Dhanalakshmi V, Bino D, a M, S. Opinion mining from student feedback data using supervised learning algorithms.; 2016; pp. 1\u20135.","DOI":"10.1109\/ICBDSC.2016.7460390"},{"key":"2526_CR54","doi-asserted-by":"crossref","unstructured":"El-Halees A. Mining opinions in user-generated contents to improve course evaluation. In: Zain JM, Wan Mohd WM bt, El-Qawasmeh E (Eds) Proceedings of the Software Engineering and Computer Systems;; Springer Berlin Heidelberg: Berlin, Heidelberg, 2011; pp. 107\u2013115.","DOI":"10.1007\/978-3-642-22191-0_9"},{"key":"2526_CR55","doi-asserted-by":"crossref","unstructured":"Altrabsheh N, Haig E, Fallahkhair S. Learning sentiment from students\u2019 feedback for real-time interventions in classrooms. 2014.","DOI":"10.1007\/978-3-319-11298-5_5"},{"key":"2526_CR56","unstructured":"Altrabsheh N, Gaber M, Haig E. SA-E: sentiment analysis for education. In: Proceedings of the Frontiers in Artificial Intelligence and Applications; 2013; vol. 255."},{"key":"2526_CR57","unstructured":"Wen M, Yang D, Ros\u00e9 C. Sentiment analysis in MOOC discussion forums: what does it tell us? In: Proceedings of the EDM; 2014."},{"key":"2526_CR58","unstructured":"Chaplot D, Rhim E, Kim J. Predicting student attrition in MOOCs using sentiment analysis and neural networks. 2015; Vol. 1432."},{"key":"2526_CR59","doi-asserted-by":"publisher","unstructured":"Farra N, Somasundaran S, Burstein J. Scoring persuasive essays using opinions and their targets. In: 10th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2015 at the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 20152015, 64\u201374, https:\/\/doi.org\/10.3115\/v1\/w15-0608.","DOI":"10.3115\/v1\/w15-0608"},{"key":"2526_CR60","doi-asserted-by":"publisher","first-page":"108486","DOI":"10.1109\/ACCESS.2019.2933354","volume":"7","author":"HK Janda","year":"2019","unstructured":"Janda HK, Pawar A, Du S, Mago V. Syntactic, semantic and sentiment analysis: the joint effect on automated essay evaluation. IEEE Access. 2019;7:108486\u2013503. https:\/\/doi.org\/10.1109\/ACCESS.2019.2933354.","journal-title":"IEEE Access"},{"key":"2526_CR61","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1002\/cae.22068","volume":"27","author":"Q Lin","year":"2019","unstructured":"Lin Q, Zhu Y, Zhang S, Shi P, Guo Q, Niu Z. Lexical based automated teaching evaluation via student_ short reviews. Comput Appl Eng Educ. 2019;27:194\u2013205.","journal-title":"Comput Appl Eng Educ"},{"key":"2526_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2019.04.007","author":"F Greco","year":"2020","unstructured":"Greco F, Polli A. Emotional text mining: customer profiling in brand management. Int J Inf Manag. 2020. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2019.04.007.","journal-title":"Int J Inf Manag"},{"key":"2526_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109885","volume":"257","author":"OAM Salem","year":"2022","unstructured":"Salem OAM, Liu F, Chen Y-PP, Hamed A, Chen X. Effective fuzzy joint mutual information feature selection based on uncertainty region for classification problem. Knowl-Based Syst. 2022;257: 109885. https:\/\/doi.org\/10.1016\/j.knosys.2022.109885.","journal-title":"Knowl-Based Syst"},{"key":"2526_CR64","doi-asserted-by":"publisher","first-page":"2795","DOI":"10.1016\/j.procs.2023.01.251","volume":"218","author":"AS Rajawat","year":"2023","unstructured":"Rajawat AS, Bedi P, Goyal SB, Bhaladhare P, Aggarwal A, Singhal RS. Fusion fuzzy logic and deep learning for depression detection using facial expressions. Procedia Comput Sci. 2023;218:2795\u2013805. https:\/\/doi.org\/10.1016\/j.procs.2023.01.251.","journal-title":"Procedia Comput Sci"},{"key":"2526_CR65","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/978-3-642-12538-6_6","volume":"284","author":"XS Yang","year":"2010","unstructured":"Yang XS. A new metaheuristic bat-inspired algorithm. Stud Comput Intell. 2010;284:65\u201374. https:\/\/doi.org\/10.1007\/978-3-642-12538-6_6.","journal-title":"Stud Comput Intell"},{"key":"2526_CR66","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.knosys.2016.02.017","volume":"100","author":"L Zhang","year":"2016","unstructured":"Zhang L, Jiang L, Li C, Kong G. Two feature weighting approaches for naive bayes text classifiers. Knowl-Based Syst. 2016;100:137\u201344. https:\/\/doi.org\/10.1016\/j.knosys.2016.02.017.","journal-title":"Knowl-Based Syst"},{"key":"2526_CR67","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.engappai.2016.02.002","volume":"52","author":"L Jiang","year":"2016","unstructured":"Jiang L, Li C, Wang S, Zhang L. Deep feature weighting for naive bayes and its application to text classification. Eng Appl Artif Intell. 2016;52:26\u201339. https:\/\/doi.org\/10.1016\/j.engappai.2016.02.002.","journal-title":"Eng Appl Artif Intell"},{"key":"2526_CR68","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-12538-3","author":"RN Rathi","year":"2022","unstructured":"Rathi RN, Mustafi A. The importance of term weighting in semantic understanding of text: a review of techniques. Multimed Tools Appl. 2022. https:\/\/doi.org\/10.1007\/s11042-022-12538-3.","journal-title":"Multimed Tools Appl"},{"key":"2526_CR69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1504\/ijbic.2018.093328","volume":"12","author":"GG Wang","year":"2018","unstructured":"Wang GG, Deb S, Dos Santos Coelho L. Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. Int J Bio-Inspired Comput. 2018;12:1\u201322. https:\/\/doi.org\/10.1504\/ijbic.2018.093328.","journal-title":"Int J Bio-Inspired Comput"},{"key":"2526_CR70","doi-asserted-by":"crossref","unstructured":"Toh Z, Way F, Wang W. DLIREC: aspect term extraction and term polarity classification system. 2014, pp 235\u2013240.","DOI":"10.3115\/v1\/S14-2038"},{"key":"2526_CR71","doi-asserted-by":"crossref","unstructured":"Cheng J, Zhao S, Zhang J, King I, Zhang X, Wang H. Aspect-level sentiment classification with HEAT (HiErarchical ATtention) network. In: Proceedings of the Proceedings of the 2017 ACM on Conference on Information and Knowledge Management; Association for Computing Machinery: New York, NY, USA, 2017; pp. 97\u2013106.","DOI":"10.1145\/3132847.3133037"},{"key":"2526_CR72","doi-asserted-by":"crossref","unstructured":"Wang Y, Huang M, Zhu X, Zhao L. Attention-based LSTM for aspect-level sentiment classification. 2016; pp. 606\u2013615.","DOI":"10.18653\/v1\/D16-1058"},{"key":"2526_CR73","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12559-018-9549-x","volume":"10","author":"Y Ma","year":"2018","unstructured":"Ma Y, Peng H, Khan T, Cambria E, Hussain A. Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis. Cogn Comput. 2018;10:1\u201312. https:\/\/doi.org\/10.1007\/s12559-018-9549-x.","journal-title":"Cogn Comput"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-02526-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-023-02526-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-02526-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T06:54:10Z","timestamp":1730962450000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-023-02526-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,2]]},"references-count":73,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["2526"],"URL":"https:\/\/doi.org\/10.1007\/s42979-023-02526-1","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,2]]},"assertion":[{"value":"16 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2024","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 confirm that there is no any conflict of interests with anyone involved.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"135"}}