{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:19:09Z","timestamp":1775229549056,"version":"3.50.1"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031282379","type":"print"},{"value":"9783031282386","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-28238-6_9","type":"book-chapter","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T17:03:18Z","timestamp":1678986198000},"page":"125-140","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Knowing What and\u00a0How: A Multi-modal Aspect-Based Framework for\u00a0Complaint Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2020-4751","authenticated-orcid":false,"given":"Apoorva","family":"Singh","sequence":"first","affiliation":[]},{"given":"Vivek","family":"Gangwar","sequence":"additional","affiliation":[]},{"given":"Shubham","family":"Sharma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5458-9381","authenticated-orcid":false,"given":"Sriparna","family":"Saha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"9_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1007\/978-3-319-75487-1_19","volume-title":"Computational Linguistics and Intelligent Text Processing","author":"MS Akhtar","year":"2018","unstructured":"Akhtar, M.S., Ekbal, A., Bhattacharyya, P.: Aspect based sentiment analysis: category detection and sentiment classification for hindi. In: Gelbukh, A. (ed.) CICLing 2016. LNCS, vol. 9624, pp. 246\u2013257. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75487-1_19"},{"issue":"4","key":"9_CR2","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1162\/coli.07-034-R2","volume":"34","author":"R Artstein","year":"2008","unstructured":"Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Comput. Linguist. 34(4), 555\u2013596 (2008)","journal-title":"Comput. Linguist."},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Bhat, S., Culotta, A.: Identifying leading indicators of product recalls from online reviews using positive unlabeled learning and domain adaptation. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 11 (2017)","DOI":"10.1609\/icwsm.v11i1.14919"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Cai, Y., Cai, H., Wan, X.: Multi-modal sarcasm detection in twitter with hierarchical fusion model. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2506\u20132515 (2019)","DOI":"10.18653\/v1\/P19-1239"},{"key":"9_CR5","doi-asserted-by":"publisher","unstructured":"Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: Wu, D., Carpuat, M., Carreras, X., Vecchi, E.M. (eds.) Proceedings of SSST@EMNLP 2014, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, 25 October 2014, pp. 103\u2013111. Association for Computational Linguistics (2014). https:\/\/doi.org\/10.3115\/v1\/W14-4012, https:\/\/www.aclweb.org\/anthology\/W14-4012\/","DOI":"10.3115\/v1\/W14-4012"},{"issue":"4","key":"9_CR6","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1016\/j.dss.2007.10.010","volume":"44","author":"K Coussement","year":"2008","unstructured":"Coussement, K., Van den Poel, D.: Improving customer complaint management by automatic email classification using linguistic style features as predictors. Decis. Supp. Syst. 44(4), 870\u2013882 (2008)","journal-title":"Decis. Supp. Syst."},{"key":"9_CR7","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2\u20137 June 2019, vol. 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"issue":"5","key":"9_CR8","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1037\/h0031619","volume":"76","author":"JL Fleiss","year":"1971","unstructured":"Fleiss, J.L.: Measuring nominal scale agreement among many raters. Psychol. Bull. 76(5), 378 (1971)","journal-title":"Psychol. Bull."},{"key":"9_CR9","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315\u2013323 (2011)"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"9_CR11","doi-asserted-by":"publisher","unstructured":"Jin, M., Aletras, N.: Complaint identification in social media with transformer networks. In: Scott, D., Bel, N., Zong, C. (eds.) Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), 8\u201313 December 2020, pp. 1765\u20131771. International Committee on Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.157, https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.157","DOI":"10.18653\/v1\/2020.coling-main.157"},{"key":"9_CR12","doi-asserted-by":"publisher","unstructured":"Jin, M., Aletras, N.: Modeling the severity of complaints in social media. In: Toutanova, K., et al. (eds.) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, 6\u201311 June 2021, pp. 2264\u20132274. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.180","DOI":"10.18653\/v1\/2021.naacl-main.180"},{"key":"9_CR13","unstructured":"Kiela, D., Bhooshan, S., Firooz, H., Perez, E., Testuggine, D.: Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950 (2019)"},{"key":"9_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Lailiyah, M., Sumpeno, S., Purnama, I.E.: Sentiment analysis of public complaints using lexical resources between Indonesian sentiment lexicon and sentiwordnet. In: 2017 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 307\u2013312. IEEE (2017)","DOI":"10.1109\/ISITIA.2017.8124100"},{"issue":"6","key":"9_CR16","doi-asserted-by":"publisher","first-page":"3522","DOI":"10.1007\/s10489-020-01964-1","volume":"51","author":"W Liao","year":"2021","unstructured":"Liao, W., Zeng, B., Yin, X., Wei, P.: An improved aspect-category sentiment analysis model for text sentiment analysis based on roberta. Appl. Intell. 51(6), 3522\u20133533 (2021)","journal-title":"Appl. Intell."},{"key":"9_CR17","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1007\/978-981-15-7670-6_26","volume-title":"Neural Computing for Advanced Applications","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Liu, H., Wong, L.-P., Lee, L.-K., Zhang, H., Hao, T.: A hybrid neural network RBERT-C based on pre-trained RoBERTa and CNN for user intent classification. In: Zhang, H., Zhang, Z., Wu, Z., Hao, T. (eds.) NCAA 2020. CCIS, vol. 1265, pp. 306\u2013319. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-7670-6_26"},{"key":"9_CR18","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1007\/978-3-030-84186-7_31","volume-title":"Chinese Computational Linguistics","author":"Z Liu","year":"2021","unstructured":"Liu, Z., Lin, W., Shi, Y., Zhao, J.: A robustly optimized BERT pre-training approach with post-training. In: Li, S., et al. (eds.) CCL 2021. LNCS (LNAI), vol. 12869, pp. 471\u2013484. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-84186-7_31"},{"key":"9_CR19","first-page":"1","volume":"32","author":"J Lu","year":"2019","unstructured":"Lu, J., Batra, D., Parikh, D., Lee, S.: Vilbert: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Adv. Neural Inf. Process. Syst. 32, 1\u201311 (2019)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"9_CR20","unstructured":"Olshtain, E., Weinbach, L.: Complaints: a study of speech act behavior among native and nonnative speakers of hebrew. the prag-matic perspective (1985)"},{"key":"9_CR21","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825\u20132830 (2011)"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., Mihalcea, R.: Meld: a multimodal multi-party dataset for emotion recognition in conversations. arXiv preprint arXiv:1810.02508 (2018)","DOI":"10.18653\/v1\/P19-1050"},{"key":"9_CR23","doi-asserted-by":"publisher","unstructured":"Preotiuc-Pietro, D., Gaman, M., Aletras, N.: Automatically identifying complaints in social media. In: Korhonen, A., Traum, D.R., M\u00e0rquez, L. (eds.) Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, 28 July\u20132 August 2019, vol. 1: Long Papers, pp. 5008\u20135019. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/p19-1495","DOI":"10.18653\/v1\/p19-1495"},{"key":"9_CR24","doi-asserted-by":"publisher","unstructured":"Saha, T., Patra, A.P., Saha, S., Bhattacharyya, P.: Towards emotion-aided multi-modal dialogue act classification. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, 5\u201310 July 2020, pp. 4361\u20134372. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.402","DOI":"10.18653\/v1\/2020.acl-main.402"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Saha, T., Upadhyaya, A., Saha, S., Bhattacharyya, P.: Towards sentiment and emotion aided multi-modal speech act classification in twitter. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 5727\u20135737 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.456"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Singh, A., Dey, S., Singha, A., Saha, S.: Sentiment and emotion-aware multi-modal complaint identification. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, 22 February\u20131 March 2022, pp. 12163\u201312171. AAAI Press (2022). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/21476","DOI":"10.1609\/aaai.v36i11.21476"},{"key":"9_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1007\/978-3-030-99736-6_29","volume-title":"Advances in Information Retrieval","author":"A Singh","year":"2022","unstructured":"Singh, A., Nazir, A., Saha, S.: Adversarial multi-task model for emotion, sentiment, and sarcasm aided complaint detection. In: Hagen, M., Verberne, S., Macdonald, C., Seifert, C., Balog, K., N\u00f8rv\u00e5g, K., Setty, V. (eds.) ECIR 2022. LNCS, vol. 13185, pp. 428\u2013442. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-99736-6_29"},{"key":"9_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1007\/978-3-030-86331-9_46","volume-title":"Document Analysis and Recognition \u2013 ICDAR 2021","author":"A Singh","year":"2021","unstructured":"Singh, A., Saha, S.: Are you really complaining? a multi-task framework for complaint identification, emotion, and sentiment classification. In: Llad\u00f3s, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12822, pp. 715\u2013731. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86331-9_46"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Singh, A., Saha, S., Hasanuzzaman, M., Dey, K.: Multitask learning for complaint identification and sentiment analysis. Cogn. Comput., 1\u201316 (2021)","DOI":"10.1007\/s12559-021-09844-7"},{"issue":"1","key":"9_CR30","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"9_CR31","unstructured":"Trosborg, A.: Interlanguage Pragmatics: Requests, Complaints, and Apologies, vol. 7. Walter de Gruyter (2011)"},{"issue":"6","key":"9_CR32","doi-asserted-by":"publisher","first-page":"1707","DOI":"10.1016\/j.pragma.2010.11.007","volume":"43","author":"C V\u00e1squez","year":"2011","unstructured":"V\u00e1squez, C.: Complaints online: the case of tripadvisor. J. Pragmat. 43(6), 1707\u20131717 (2011)","journal-title":"J. Pragmat."},{"key":"9_CR33","unstructured":"Vaswani, A., et al.: Attention is all you need. CoRR abs\/1706.03762 (2017). http:\/\/arxiv.org\/abs\/1706.03762"},{"key":"9_CR34","unstructured":"Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)"},{"issue":"1\u20132","key":"9_CR35","first-page":"28","volume":"34","author":"BL Welch","year":"1947","unstructured":"Welch, B.L.: The generalization of \u2018student\u2019s\u2019problem when several different population varlances are involved. Biometrika 34(1\u20132), 28\u201335 (1947)","journal-title":"Biometrika"},{"key":"9_CR36","doi-asserted-by":"publisher","unstructured":"Yang, W., et al.: Detecting customer complaint escalation with recurrent neural networks and manually-engineered features. In: Loukina, A., Morales, M., Kumar, R. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2\u20137 June 2019, vol. 2 (Industry Papers), pp. 56\u201363. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/n19-2008","DOI":"10.18653\/v1\/n19-2008"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-28238-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T13:46:20Z","timestamp":1709646380000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-28238-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031282379","9783031282386"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-28238-6_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"45","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2023.org\/index.html?v=1.0","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":"489","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":"77","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":"83","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":"16% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}