{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:14:10Z","timestamp":1742958850352,"version":"3.40.3"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031165634"},{"type":"electronic","value":"9783031165641"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16564-1_1","type":"book-chapter","created":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T23:02:43Z","timestamp":1664146963000},"page":"3-12","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Granular Emotion Detection in\u00a0Social Media Using Multi-Discipline Ensembles"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9874-1186","authenticated-orcid":false,"given":"Robert H.","family":"Frye","sequence":"first","affiliation":[]},{"given":"David C.","family":"Wilson","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,26]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Al-Omari, H., et al.: EmoDet at SemEval-2019 task 3: emotion detection in text using deep learning. In: Proceedings of the 13th International Workshop on Semantic Evaluation (2019)","DOI":"10.18653\/v1\/S19-2032"},{"key":"1_CR2","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. 77, 236\u2013246 (2017)","journal-title":"Expert Syst. Appl."},{"key":"1_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.tele.2020.101345","volume":"48","author":"M Asif","year":"2020","unstructured":"Asif, M., et al.: Sentiment analysis of extremism in social media from textual information. Telematics Inform. 48, 101345 (2020)","journal-title":"Telematics Inform."},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Baziotis, C., et al.: Ntua-slp at semeval-2018 task 3: tracking ironic tweets using ensembles of word and character level attentive RNNs. arXiv:1804.06659 (2018)","DOI":"10.18653\/v1\/S18-1100"},{"key":"1_CR5","unstructured":"Bickerstaffe, A., Zukerman, I.: A hierarchical classifier applied to multi-way sentiment detection. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 62\u201370. Association for Computational Linguistics (2010)"},{"issue":"2","key":"1_CR6","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123\u2013140 (1996)","journal-title":"Mach. Learn."},{"issue":"4","key":"1_CR7","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1023\/A:1021240730564","volume":"12","author":"R Burke","year":"2002","unstructured":"Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331\u2013370 (2002)","journal-title":"User Model. User-Adap. Inter."},{"key":"1_CR8","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.osnem.2017.08.001","volume":"2","author":"P Burnap","year":"2017","unstructured":"Burnap, P., et al.: Multi-class machine classification of suicide-related communication on twitter. Online Soc. Networks Media 2, 32\u201344 (2017)","journal-title":"Online Soc. Networks Media"},{"key":"1_CR9","unstructured":"Cao, M.D., Zukerman, I.: Experimental evaluation of a lexicon-and corpus-based ensemble for multi-way sentiment analysis. In: Proceedings of the Australasian Language Technology Association Workshop 2012, pp. 52\u201360 (2012)"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"1_CR11","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)"},{"key":"1_CR12","unstructured":"Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: ELECTRA: pre-training text encoders as discriminators rather than generators (2020)"},{"key":"1_CR13","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.dss.2014.07.003","volume":"66","author":"NF Da Silva","year":"2014","unstructured":"Da Silva, N.F., Hruschka, E.R., Hruschka, E.R., Jr.: Tweet sentiment analysis with classifier ensembles. Decis. Support Syst. 66, 170\u2013179 (2014)","journal-title":"Decis. Support Syst."},{"key":"1_CR14","unstructured":"De Choudhury, M., et al.: Predicting depression via social media. In: Seventh international AAAI conference on weblogs and social media (2013)"},{"key":"1_CR15","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding (2019)"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Duin, R.P.: Classifiers in almost empty spaces. In: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, vol. 2, pp. 1\u20137. IEEE (2000)","DOI":"10.1109\/ICPR.2000.906006"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Duppada, V., Jain, R., Hiray, S.: Seernet at semeval-2018 task 1: domain adaptation for affect in tweets. arXiv preprint arXiv:1804.06137 (2018)","DOI":"10.18653\/v1\/S18-1002"},{"issue":"352","key":"1_CR18","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1080\/01621459.1975.10480319","volume":"70","author":"B Efron","year":"1975","unstructured":"Efron, B.: The efficiency of logistic regression compared to normal discriminant analysis. J. Am. Stat. Assoc. 70(352), 892\u2013898 (1975)","journal-title":"J. Am. Stat. Assoc."},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Ekman, P.: Basic emotions. In: Handbook of Cognition and Emotion, pp. 45\u201360 (1999)","DOI":"10.1002\/0470013494.ch3"},{"issue":"1","key":"1_CR20","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119\u2013139 (1997)","journal-title":"J. Comput. Syst. Sci."},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Frye, R.H., Wilson, D.C.: Comparative analysis of transformers to support fine-grained emotion detection in short-text data. In: The Thirty-Fifth International Flairs Conference (2022)","DOI":"10.32473\/flairs.v35i.130612"},{"key":"1_CR22","unstructured":"Ghosh, S., Vinyals, O., Strope, B., Roy, S., Dean, T., Heck, L.: Contextual LSTM (CLSTM) models for large scale NLP tasks. arXiv preprint arXiv:1602.06291 (2016)"},{"issue":"3","key":"1_CR23","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1111\/j.1467-8640.2012.00454.x","volume":"29","author":"N Gupta","year":"2013","unstructured":"Gupta, N., Gilbert, M., Fabbrizio, G.D.: Emotion detection in email customer care. Comput. Intell. 29(3), 489\u2013505 (2013)","journal-title":"Comput. Intell."},{"key":"1_CR24","unstructured":"Gupta, S.: Applications of sentiment analysis in business. Towards Data Science. https:\/\/towardsdatascience.com\/applications-of-sentiment-analysis-in-business-b7e660e3de69"},{"key":"1_CR25","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1109\/34.58871","volume":"10","author":"LK Hansen","year":"1990","unstructured":"Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 10, 993\u20131001 (1990)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"1_CR26","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"1_CR27","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.eswa.2017.07.019","volume":"94","author":"M Kang","year":"2018","unstructured":"Kang, M., Ahn, J., Lee, K.: Opinion mining using ensemble text hidden Markov models for text classification. Expert Syst. Appl. 94, 218\u2013227 (2018)","journal-title":"Expert Syst. Appl."},{"key":"1_CR28","unstructured":"Khan, J.: Sentiment analysis : Key to empathetic customer service. Ameyo. https:\/\/www.ameyo.com\/blog\/sentiment-analysis-key-to-empathetic-customer-service"},{"key":"1_CR29","unstructured":"Lample, G., Conneau, A.: Cross-lingual language model pretraining (2019)"},{"key":"1_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/3-540-46805-6_19","volume-title":"Shape, Contour and Grouping in Computer Vision","author":"Y LeCun","year":"1999","unstructured":"LeCun, Y., Haffner, P., Bottou, L., Bengio, Y.: Object recognition with gradient-based learning. In: Shape, Contour and Grouping in Computer Vision. LNCS, vol. 1681, pp. 319\u2013345. Springer, Heidelberg (1999). https:\/\/doi.org\/10.1007\/3-540-46805-6_19"},{"key":"1_CR31","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized Bert pretraining approach (2019)"},{"key":"1_CR32","doi-asserted-by":"crossref","unstructured":"Oussous, A., Lahcen, A.A., Belfkih, S.: Impact of text pre-processing and ensemble learning on Arabic sentiment analysis. In: Proceedings of the 2nd International Conference on Networking, Information Systems & Security, p. 65. ACM (2019)","DOI":"10.1145\/3320326.3320399"},{"key":"1_CR33","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.engappai.2016.01.012","volume":"51","author":"I Perikos","year":"2016","unstructured":"Perikos, I., Hatzilygeroudis, I.: Recognizing emotions in text using ensemble of classifiers. Eng. Appl. Artif. Intell. 51, 191\u2013201 (2016)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"15","key":"1_CR34","doi-asserted-by":"publisher","first-page":"11007","DOI":"10.1007\/s00500-019-04310-x","volume":"24","author":"FA Pujol","year":"2019","unstructured":"Pujol, F.A., Mora, H., Pertegal, M.L.: A soft computing approach to violence detection in social media for smart cities. Soft. Comput. 24(15), 11007\u201311017 (2019). https:\/\/doi.org\/10.1007\/s00500-019-04310-x","journal-title":"Soft. Comput."},{"key":"1_CR35","doi-asserted-by":"crossref","unstructured":"Ramadhan, W., Novianty, S.A., Setianingsih, S.C.: Sentiment analysis using multinomial logistic regression. In: 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), pp. 46\u201349. IEEE (2017)","DOI":"10.1109\/ICCEREC.2017.8226700"},{"key":"1_CR36","doi-asserted-by":"crossref","unstructured":"Ranganathan, J., Hedge, N., Irudayaraj, A., Tzacheva, A.: Automatic detection of emotions in twitter data-a scalable decision tree classification method. In: Proceedings of the RevOpID 2018 Workshop on Opinion Mining, Summarization and Diversification in 29th ACM Conference on Hypertext and Social Media (2018)","DOI":"10.1145\/3301020.3303751"},{"issue":"2","key":"1_CR37","first-page":"197","volume":"5","author":"RE Schapire","year":"1990","unstructured":"Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5(2), 197\u2013227 (1990)","journal-title":"Mach. Learn."},{"issue":"11","key":"1_CR38","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673\u20132681 (1997)","journal-title":"IEEE Trans. Signal Process."},{"key":"1_CR39","doi-asserted-by":"crossref","unstructured":"Smetanin, S.: Emosense at semeval-2019 task 3: Bidirectional LSTM network for contextual emotion detection in textual conversations. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 210\u2013214 (2019)","DOI":"10.18653\/v1\/S19-2034"},{"key":"1_CR40","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.eswa.2018.06.022","volume":"110","author":"S Symeonidis","year":"2018","unstructured":"Symeonidis, S., et al.: A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis. Expert Syst. Appl. 110, 298\u2013310 (2018)","journal-title":"Expert Syst. Appl."},{"key":"1_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3264-1","volume-title":"The nature of statistical learning theory","author":"V Vapnik","year":"2000","unstructured":"Vapnik, V.: The nature of statistical learning theory. Springer, New York (2000). https:\/\/doi.org\/10.1007\/978-1-4757-3264-1"},{"key":"1_CR42","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)"},{"key":"1_CR43","unstructured":"Walther, C.: Sentiment analysis in marketing: What are you waiting for? CMS Wire. https:\/\/www.cmswire.com\/digital-marketing\/sentiment-analysis-in-marketing-what-are-you-waiting-for\/"},{"key":"1_CR44","doi-asserted-by":"crossref","unstructured":"Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: Glue: a multi-task benchmark and analysis platform for natural language understanding (2019)","DOI":"10.18653\/v1\/W18-5446"},{"key":"1_CR45","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Harnessing twitter \u201cbig data\u201d for automatic emotion identification. In: 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Conference on Social Computing, pp. 587\u2013592. IEEE (2012)","DOI":"10.1109\/SocialCom-PASSAT.2012.119"},{"key":"1_CR46","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: A novel hybrid mobile malware detection system integrating anomaly detection with misuse detection. In: Proceedings of the 6th International Workshop on Mobile Cloud Computing and Services, pp. 15\u201322. ACM (2015)","DOI":"10.1145\/2802130.2802132"},{"key":"1_CR47","unstructured":"Wolfe, J.: Want faster airline customer service? try tweeting. The New York Times. https:\/\/www.nytimes.com\/2018\/11\/20\/travel\/airline-customer-service-twitter.html"},{"issue":"6","key":"1_CR48","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1016\/j.ins.2010.11.023","volume":"181","author":"R Xia","year":"2011","unstructured":"Xia, R., Zong, C., Li, S.: Ensemble of feature sets and classification algorithms for sentiment classification. Inf. Sci. 181(6), 1138\u20131152 (2011)","journal-title":"Inf. Sci."},{"key":"1_CR49","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: Xlnet: generalized autoregressive pretraining for language understanding (2020)"},{"issue":"2","key":"1_CR50","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1007\/s10115-018-1236-4","volume":"60","author":"L Yue","year":"2018","unstructured":"Yue, L., Chen, W., Li, X., Zuo, W., Yin, M.: A survey of sentiment analysis in social media. Knowl. Inf. Syst. 60(2), 617\u2013663 (2018). https:\/\/doi.org\/10.1007\/s10115-018-1236-4","journal-title":"Knowl. Inf. Syst."},{"key":"1_CR51","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/978-3-319-99501-4_37","volume-title":"Natural Language Processing and Chinese Computing","author":"T Yue","year":"2018","unstructured":"Yue, T., Chen, C., Zhang, S., Lin, H., Yang, L.: Ensemble of neural networks with sentiment words translation for code-switching emotion detection. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds.) NLPCC 2018. LNCS (LNAI), vol. 11109, pp. 411\u2013419. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99501-4_37"},{"issue":"4","key":"1_CR52","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1253","volume":"8","author":"L Zhang","year":"2018","unstructured":"Zhang, L., et al.: Deep learning for sentiment analysis: a survey. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 8(4), e1253 (2018)","journal-title":"Wiley Interdisc. Rev. Data Min. Knowl. Discov."}],"container-title":["Lecture Notes in Computer Science","Foundations of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16564-1_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T16:07:39Z","timestamp":1728058059000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16564-1_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031165634","9783031165641"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16564-1_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"26 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISMIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Methodologies for Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cosenza","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ismis2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ismis2022.icar.cnr.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"71","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"44% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.7","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Number and type of other papers accepted : 4 industrial papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}