{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T22:59:00Z","timestamp":1773615540821,"version":"3.50.1"},"reference-count":26,"publisher":"Allerton Press","issue":"3","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"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":["Aut. Control Comp. Sci."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.3103\/s0146411625700555","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T11:22:52Z","timestamp":1757330572000},"page":"389-401","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Stack Ensemble Learning Techniques for Sentiment Classification in Climate Change Discourse"],"prefix":"10.3103","volume":"59","author":[{"family":"Vipin Jain","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shilpa Agnihotri","family":"Pandey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ankit","family":"Chakrawarti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rahul","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinod","family":"Patidar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1627","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"7844_CR1","doi-asserted-by":"publisher","first-page":"4723","DOI":"10.3390\/su14084723","volume":"14","author":"N.M. Sham","year":"2022","unstructured":"Sham, N.M. and Mohamed, A., Climate change sentiment analysis using lexicon, machine learning and hybrid approaches, Sustainability, 2022, vol. 14, no. 8, p. 4723. https:\/\/doi.org\/10.3390\/su14084723","journal-title":"Sustainability"},{"key":"7844_CR2","first-page":"35","volume":"63","author":"D.W. Titley","year":"2010","unstructured":"Titley, D.W. and St. John, C., Arctic security considerations and the US navy\u2019s roadmap for the arctic, Nav. War Coll. Rev., 2010, vol. 63, no. 2, pp. 35\u201348.","journal-title":"Nav. War Coll. Rev."},{"key":"7844_CR3","doi-asserted-by":"publisher","first-page":"16839","DOI":"10.1007\/s11042-022-13937-2","volume":"82","author":"V. Jain","year":"2023","unstructured":"Jain, V. and Kashyap, K.L., Ensemble hybrid model for Hindi COVID-19 text classification with metaheuristic optimization algorithm, Multimedia Tools Appl., 2023, vol. 82, no. 11, pp. 16839\u201316859. https:\/\/doi.org\/10.1007\/s11042-022-13937-2","journal-title":"Multimedia Tools Appl."},{"key":"7844_CR4","doi-asserted-by":"publisher","unstructured":"Cross-Cultural Design: 15th International Conference, CCD 2023, Held as Part of the 25th International Conference, HCII 2023, Copenhagen, Denmark, July 23\u201328, \n               2023, Proceedings, Part III, Rau, P.-L.P., Ed., Lecture Notes in Computer Science, vol. 14024, Cham: Springer, 2023. https:\/\/doi.org\/10.1007\/978-3-031-35946-0","DOI":"10.1007\/978-3-031-35946-0"},{"key":"7844_CR5","first-page":"17","volume":"8","author":"M. Ahmad","year":"2017","unstructured":"Ahmad, M., Aftab, S., Muhammad, S.S., and Waheed, U., Tools and techniques for lexicon driven sentiment analysis: A review, Int. J. Multidiscip. Sci. Eng., 2017, vol. 8, no. 1, pp. 17\u201323.","journal-title":"Int. J. Multidiscip. Sci. Eng."},{"key":"7844_CR6","doi-asserted-by":"publisher","first-page":"e1378","DOI":"10.7717\/peerj-cs.1378","volume":"9","author":"T. Hariguna","year":"2023","unstructured":"Hariguna, T. and Ruangkanjanases, A., Adaptive sentiment analysis using multioutput classification: A performance comparison, PeerJ Comput. Sci., 2023, vol. 9, p. e1378. https:\/\/doi.org\/10.7717\/peerj-cs.1378","journal-title":"PeerJ Comput. Sci."},{"key":"7844_CR7","doi-asserted-by":"publisher","unstructured":"Garrido-Merch\u2019an, E.C., Gonz\u2019alez-Barthe, C., and Vaca, M.C., Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks, arXiv Preprint, 2023. https:\/\/doi.org\/10.48550\/arXiv.2303.13373","DOI":"10.48550\/arXiv.2303.13373"},{"key":"7844_CR8","doi-asserted-by":"publisher","unstructured":"Ray, S. and Senthil Kumar, A.M., Prediction and analysis of sentiments of Reddit users towards the climate change crisis, 2023 International Conference on Networking and Communications (ICNWC), Chennai, India, 2023, IEEE, 2023, pp. 1\u201317. https:\/\/doi.org\/10.1109\/icnwc57852.2023.10127496","DOI":"10.1109\/icnwc57852.2023.10127496"},{"key":"7844_CR9","doi-asserted-by":"publisher","first-page":"e0136092","DOI":"10.1371\/journal.pone.0136092","volume":"10","author":"E.M. Cody","year":"2015","unstructured":"Cody, E.M., Reagan, A.J., Mitchell, L., Dodds, P.Sh., and Danforth, Ch.M., Climate change sentiment on Twitter: An unsolicited public opinion poll, PLoS One, 2015, vol. 10, no. 8, p. e0136092. https:\/\/doi.org\/10.1371\/journal.pone.0136092","journal-title":"PLoS One"},{"key":"7844_CR10","doi-asserted-by":"publisher","first-page":"103325","DOI":"10.1016\/j.ipm.2023.103325","volume":"60","author":"A. Upadhyaya","year":"2023","unstructured":"Upadhyaya, A., Fisichella, M., and Nejdl, W., Towards sentiment and temporal aided stance detection of climate change tweets, Inf. Process. Manage., 2023, vol. 60, no. 4, p. 103325. https:\/\/doi.org\/10.1016\/j.ipm.2023.103325","journal-title":"Inf. Process. Manage."},{"key":"7844_CR11","doi-asserted-by":"publisher","first-page":"1904172","DOI":"10.1155\/2020\/1904172","volume":"2020","author":"Ya. Kirelli","year":"2020","unstructured":"Kirelli, Ya. and Arslankaya, S., Sentiment analysis of shared tweets on global warming on Twitter with data mining methods: A case study on Turkish language, Computational Intelligence and Neuroscience, 2020, vol. 2020, p.\u00a01904172. https:\/\/doi.org\/10.1155\/2020\/1904172","journal-title":"Computational Intelligence and Neuroscience"},{"key":"7844_CR12","unstructured":"Wang, Z., Sentiment analysis of climate change using Twitter API and machine learning, Bachelor\u2019s Thesis, Turku, Finland: Turku University of Applied Sciences, 2020."},{"key":"7844_CR13","doi-asserted-by":"publisher","first-page":"106697","DOI":"10.1016\/j.resconrec.2022.106697","volume":"188","author":"M. Wu","year":"2023","unstructured":"Wu, M., Long, R., Chen, F., Chen, H., Bai, Yu., Cheng, K., and Huang, H., Spatio-temporal difference analysis in climate change topics and sentiment orientation: Based on LDA and BiLSTM model, Resour., Conserv. Recycling, 2023, vol. 188, p. 106697. https:\/\/doi.org\/10.1016\/j.resconrec.2022.106697","journal-title":"Resour., Conserv. Recycling"},{"key":"7844_CR14","doi-asserted-by":"publisher","first-page":"127820","DOI":"10.1016\/j.jclepro.2021.127820","volume":"312","author":"M. El Barachi","year":"2021","unstructured":"El Barachi, M., Alkhatib, M., Mathew, S., and Oroumchian, F., A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change, J. Cleaner Prod., 2021, vol. 312, p. 127820. https:\/\/doi.org\/10.1016\/j.jclepro.2021.127820","journal-title":"J. Cleaner Prod."},{"key":"7844_CR15","unstructured":"data.world: Weather sentiment data, 2016. https:\/\/data.world\/crowdflower\/weather-sentiment. Cited September 17, 2023."},{"key":"7844_CR16","unstructured":"GATE\u2019s: Earth Hour 2015 corpus, 2016. https:\/\/gate.ac.uk\/projects\/decarbonet\/datasets.html. Cited September 17, 2023."},{"key":"7844_CR17","unstructured":"Maynard, D. and Bontcheva, K., Challenges of evaluating sentiment analysis tools on social media, Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC\u20192016), Portoro\u017e, Slovenia, 2016, Calzolari, N., Choukri, Kh., Declerck, T., Goggi, S., Grobelnik, M., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., and Piperidis, S., Eds., European Language Resources Association, 2016, pp. 1142\u20131148. https:\/\/aclanthology.org\/L16-1182\/."},{"key":"7844_CR18","unstructured":"kaggle: 2020 climate sentiment on Tweeter, 2020. https:\/\/www.kaggle.com\/datasets\/joseguzman\/climate-sentiment-in-twitter. Cited September 17, 2023."},{"key":"7844_CR19","doi-asserted-by":"publisher","first-page":"6307","DOI":"10.3233\/jifs-220279","volume":"43","author":"V. Jain","year":"2022","unstructured":"Jain, V. and Kashyap, K.L., Multilayer hybrid ensemble machine learning model for analysis of Covid-19 vaccine sentiments, J. Intell. Fuzzy Syst., 2022, vol. 43, no. 5, pp. 6307\u20136319. https:\/\/doi.org\/10.3233\/jifs-220279","journal-title":"J. Intell. Fuzzy Syst."},{"key":"7844_CR20","doi-asserted-by":"publisher","first-page":"52177","DOI":"10.1109\/access.2021.3069001","volume":"9","author":"N.S.M. Nafis","year":"2021","unstructured":"Nafis, N.S.M. and Awang, S., An enhanced hybrid feature selection technique using term frequency-inverse document frequency and support vector machine-recursive feature elimination for sentiment classification, IEEE Access, 2021, vol. 9, pp. 52177\u201352192. https:\/\/doi.org\/10.1109\/access.2021.3069001","journal-title":"IEEE Access"},{"key":"7844_CR21","unstructured":"Yahia, A., Deep learning-based model for covid-19 fake news detection, PhD Dissertation, Ouargla, Algeria: University of Kasdi Merbah Ouargla, 2022."},{"key":"7844_CR22","doi-asserted-by":"publisher","first-page":"095001","DOI":"10.1088\/1361-6420\/aac287","volume":"34","author":"Q. Zhou","year":"2018","unstructured":"Zhou, Q., Liu, W., Li, J., and Marzouk, Yo.M., An approximate empirical Bayesian method for large-scale linear-Gaussian inverse problems, Inverse Probl., 2018, vol. 34, no. 9, p. 095001. https:\/\/doi.org\/10.1088\/1361-6420\/aac287","journal-title":"Inverse Probl."},{"key":"7844_CR23","doi-asserted-by":"publisher","first-page":"731","DOI":"10.3233\/jifs-224086","volume":"45","author":"V. Jain","year":"2023","unstructured":"Jain, V. and Kashyap, K.L., Analyzing research trends of sentiment analysis and its applications for Coronavirus disease (COVID-19): A systematic review, J. Intell. Fuzzy Syst., 2023, vol. 45, no. 1, pp. 731\u2013742. https:\/\/doi.org\/10.3233\/jifs-224086","journal-title":"J. Intell. Fuzzy Syst."},{"key":"7844_CR24","doi-asserted-by":"publisher","unstructured":"Olabenjo, B., Applying naive Bayes classification to Google Play apps categorization, arXiv Preprint, 2016. https:\/\/doi.org\/10.48550\/arXiv.1608.08574","DOI":"10.48550\/arXiv.1608.08574"},{"key":"7844_CR25","doi-asserted-by":"publisher","unstructured":"Jain, V. and Kashyap, K.L., Text classification using hybridization of meta-heuristic algorithm with neural network, Machine Vision and Augmented Intelligence: Select Proceedings of MAI 2022, Lecture Notes in Electrical Engineering, vol. 1007, Singapore: Springer, 2023, pp. 165\u2013173. https:\/\/doi.org\/10.1007\/978-981-99-0189-0_10","DOI":"10.1007\/978-981-99-0189-0_10"},{"key":"7844_CR26","doi-asserted-by":"publisher","unstructured":"Rao, M.Ch., Yelavarti, K.Ch., and Kalyan, N.P., A framework for hate speech detection using different ML algorithms, 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2023, IEEE, 2023, pp. 960\u2013967. https:\/\/doi.org\/10.1109\/icoei56765.2023.10125942","DOI":"10.1109\/icoei56765.2023.10125942"}],"container-title":["Automatic Control and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.3103\/S0146411625700555.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.3103\/S0146411625700555","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.3103\/S0146411625700555.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T22:01:19Z","timestamp":1773612079000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.3103\/S0146411625700555"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":26,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["7844"],"URL":"https:\/\/doi.org\/10.3103\/s0146411625700555","relation":{},"ISSN":["0146-4116","1558-108X"],"issn-type":[{"value":"0146-4116","type":"print"},{"value":"1558-108X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"25 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors of this work declare that they have no conflicts of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"CONFLICT OF INTEREST"}}]}}