{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T07:03:58Z","timestamp":1751267038247,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031194955"},{"type":"electronic","value":"9783031194962"}],"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\/978-3-031-19496-2_17","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T05:03:19Z","timestamp":1666414999000},"page":"227-235","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Sequential Models for\u00a0Sentiment Analysis: A Comparative Study"],"prefix":"10.1007","author":[{"given":"Olaronke Oluwayemisi","family":"Adebanji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irina","family":"Gelbukh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiram","family":"Calvo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olumide Ebenezer","family":"Ojo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"17_CR1","unstructured":"Aroyehun, S.T., Gelbukh, A.: Aggression detection in social media: using deep neural networks, data augmentation, and pseudo labeling. In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pp. 90\u201397. Association for Computational Linguistics, Santa Fe, New Mexico, USA, August 2018"},{"key":"17_CR2","doi-asserted-by":"publisher","unstructured":"Aroyehun, S.T., Gelbukh, A.: Detection of adverse drug reaction in tweets using a combination of heterogeneous word embeddings. In: Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pp. 133\u2013135. Association for Computational Linguistics, Florence, Italy, August 2019. https:\/\/doi.org\/10.18653\/v1\/W19-3224, https:\/\/aclanthology.org\/W19-3224","DOI":"10.18653\/v1\/W19-3224"},{"key":"17_CR3","doi-asserted-by":"publisher","unstructured":"Ashraf, N., Mustafa, R., Sidorov, G., Gelbukh, A.F.: Individual vs. group violent threats classification in online discussions. In: Companion of The 2020 Web Conference 2020, Taipei, Taiwan, 20\u201324 April 2020, pp. 629\u2013633. ACM\/IW3C2 (2020). https:\/\/doi.org\/10.1145\/3366424.3385778","DOI":"10.1145\/3366424.3385778"},{"key":"17_CR4","doi-asserted-by":"publisher","unstructured":"Clarke, I., Grieve, J.: Stylistic variation on the Donald Trump twitter account: a linguistic analysis of tweets posted between 2009 and 2018. PLOS ONE 14, 1\u201327 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0222062","DOI":"10.1371\/journal.pone.0222062"},{"key":"17_CR5","unstructured":"Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Processing 1\u20136 (2009)"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Han, W., Chen, H., Gelbukh, A., Zadeh, A., Morency, L.P., Poria, S.: Bi-bimodal modality fusion for correlation-controlled multimodal sentiment analysis. In: Proceedings of the 2021 International Conference on Multimodal Interaction, pp. 6\u201315. ICMI 2021. Association for Computing Machinery, New York, NY, USA (2021)","DOI":"10.1145\/3462244.3479919"},{"key":"17_CR7","doi-asserted-by":"publisher","unstructured":"Hern\u00e1ndez-Casta\u00f1eda, A., Calvo, H., Gelbukh, A., Flores, J.J.: Cross-domain deception detection using support vector networks. Soft Comput. 21(3), 585\u2013595 (2017). https:\/\/doi.org\/10.1007\/s00500-016-2409-2","DOI":"10.1007\/s00500-016-2409-2"},{"key":"17_CR8","unstructured":"Hoang, T.T., Ojo, O.E., Adebanji, O.O., Calvo, H., Gelbukh, A.: The combination of BERT and data oversampling for answer type prediction. In: CEUR Workshop Proceedings, vol. 3119. CEUR-WS (2022)"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Kolesnikova, O., Gelbukh, A.: Supervised machine learning for predicting the meaning of verb-noun combinations in Spanish. In: MICAI (2010)","DOI":"10.1007\/978-3-642-16773-7_17"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Kolesnikova, O., Gelbukh, A.: A study of lexical function detection with word2vec and supervised machine learning. J. Intell. Fuzzy Syst. 39 (2020)","DOI":"10.3233\/JIFS-179866"},{"key":"17_CR11","doi-asserted-by":"publisher","unstructured":"Majumder, N., Poria, S., Hazarika, D., Mihalcea, R., Gelbukh, A., Cambria, E.: DialogueRNN: an attentive RNN for emotion detection in conversations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33(01), pp. 6818\u20136825 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33016818","DOI":"10.1609\/aaai.v33i01.33016818"},{"key":"17_CR12","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs\/1301.3781 (2013). http:\/\/dblp.uni-trier.de\/db\/journals\/corr\/corr1301.html#abs-1301-3781"},{"key":"17_CR13","doi-asserted-by":"publisher","unstructured":"Ojo, O.E., Gelbukh, A., Calvo, H., Adebanji, O.O.: Performance study of n-grams in the analysis of sentiments. J. Nigerian Soc. Phys. Sci. 3(4), 477\u2013483 (2021). https:\/\/doi.org\/10.46481\/jnsps.2021.201","DOI":"10.46481\/jnsps.2021.201"},{"key":"17_CR14","doi-asserted-by":"publisher","unstructured":"Ojo, O.E., Gelbukh, A., Calvo, H., Adebanji, O.O., Sidorov, G.: Sentiment detection in economics texts. In: Advances in Computational Intelligence: 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Mexico City, Mexico, 12\u201317 October 2020, Proceedings, Part II, pp. 271\u2013281. Springer-Verlag, Berlin, Heidelberg (2020). https:\/\/doi.org\/10.1007\/978-3-030-60887-3_24","DOI":"10.1007\/978-3-030-60887-3_24"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Ojo, O.E., Hoang, T.T., Gelbukh, A., Calvo, H., Sidorov, G., Adebanji, O.O.: Automatic hate speech detection using CNN model and word embedding. Computaci\u00f3n y Sistemas 26(2) (2022)","DOI":"10.13053\/cys-26-2-4107"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Poria, S., Cambria, E., Gelbukh, A.: Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis. In: EMNLP (2015)","DOI":"10.18653\/v1\/D15-1303"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513\u2013523 (1988)","DOI":"10.1016\/0306-4573(88)90021-0"},{"issue":"11","key":"17_CR18","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673\u20132681 (1997). https:\/\/doi.org\/10.1109\/78.650093","journal-title":"IEEE Trans. Signal Process."},{"key":"17_CR19","doi-asserted-by":"publisher","unstructured":"Sidorov, G., et al.: Empirical study of machine learning based approach for opinion mining in tweets. In: Batyrshin, I., Gonz\u00e1lez Mendoza, M. (eds.) MICAI 2012. LNCS (LNAI), vol. 7629, pp. 1\u201314. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-37807-2_1","DOI":"10.1007\/978-3-642-37807-2_1"},{"key":"17_CR20","doi-asserted-by":"publisher","unstructured":"Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., Chanona-Hern\u00e1ndez, L.: Syntactic N-grams as machine learning features for natural language processing. Expert Syst. Appl. 41(3), 853\u2013860 (2014). https:\/\/doi.org\/10.1016\/j.eswa.2013.08.015","DOI":"10.1016\/j.eswa.2013.08.015"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19496-2_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T07:11:55Z","timestamp":1666422715000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19496-2_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031194955","9783031194962"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19496-2_17","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":"23 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Monterrey","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","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":"24 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micai.org\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"137","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":"63","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":"0","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":"46% - 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","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":"3","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":"17 External reviewers","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)"}}]}}