{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T19:38:53Z","timestamp":1783107533521,"version":"3.54.6"},"reference-count":48,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Social media platforms have become a widely used medium for individuals to express complex and multifaceted emotions. Traditional single-label emotion classification methods fall short in accurately capturing the simultaneous presence of multiple emotions within these texts. To address this limitation, we propose a classification model that enhances the pre-trained Cardiff NLP transformer by integrating additional self-attention layers. Experimental results show our approach achieves a micro-F1 score of 0.7208, a macro-F1 score of 0.6192, and an average Jaccard index of 0.6066, which is an overall improvement of approximately 3.00% compared to the baseline. We apply this model to a real-world dataset of tweets related to the 2011 Christchurch earthquakes as a case study to demonstrate its ability to capture multi-category emotional expressions and detect co-occurring emotions that single-label approaches would miss. Our analysis revealed distinct emotional patterns aligned with key seismic events, including overlapping positive and negative emotions, and temporal dynamics of emotional response. This work contributes a robust method for fine-grained emotion analysis which can aid disaster response, mental health monitoring and social research.<\/jats:p>","DOI":"10.3390\/informatics12040114","type":"journal-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T08:41:08Z","timestamp":1761122468000},"page":"114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Leveraging Transformer with Self-Attention for Multi-Label Emotion Classification in Crisis Tweets"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4991-3340","authenticated-orcid":false,"given":"Patricia","family":"Anthony","sequence":"first","affiliation":[{"name":"Centre for Geospatial and Computing Technologies, Lincoln University, Lincoln 7647, Canterbury, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1867-7178","authenticated-orcid":false,"given":"Jing","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Communication University of China, Beijing 100024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,22]]},"reference":[{"key":"ref_1","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is All you Need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_2","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_3","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv."},{"key":"ref_4","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Nguyen, D.Q., Vu, T., and Tuan Nguyen, A. (2020, January 16\u201320). BERTweet: A pre-trained language model for English Tweets. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Online.","DOI":"10.18653\/v1\/2020.emnlp-demos.2"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Camacho-collados, J., Rezaee, K., Riahi, T., Ushio, A., Loureiro, D., Antypas, D., Boisson, J., Espinosa Anke, L., Liu, F., and Mart\u00ednez C\u00e1mara, E. (2022, January 7\u201311). TweetNLP: Cutting-Edge Natural Language Processing for Social Media. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Abu Dhabi, United Arab Emirates.","DOI":"10.18653\/v1\/2022.emnlp-demos.5"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.patrec.2016.12.009","article-title":"Lexicon based feature extraction for emotion text classification","volume":"93","author":"Bandhakavi","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gimenez, R., Gaviola, M., Sabellano, M.J., and Gorro, K. (2017, January 28\u201330). Emotion Classification of Duterte Administration Tweets Using Hybrid Approach. Proceedings of the 2017 International Conference on Software and e-Business, Hong Kong.","DOI":"10.1145\/3178212.3178233"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"39313","DOI":"10.1109\/ACCESS.2022.3165621","article-title":"Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model","volume":"10","author":"Aslam","year":"2022","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2645381","DOI":"10.1155\/2022\/2645381","article-title":"Text-Based Emotion Recognition Using Deep Learning Approach","volume":"2022","author":"Bharti","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9567","DOI":"10.1007\/s00521-022-08186-1","article-title":"Emotion classification of Indonesian Tweets using Bidirectional LSTM","volume":"35","author":"Glenn","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Anbazhagan, K., Kurlekar, S., Brindha, T.V., and Sudhish Reddy, D. (2024, January 22\u201323). Twitter Based Emotion Recognition Using Bi-LSTM. Proceedings of the 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies, Pune, India.","DOI":"10.1109\/TQCEBT59414.2024.10545185"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1007\/s13278-025-01439-6","article-title":"Exploring emotion classification of indonesian tweets using large scale transfer learning via IndoBERT","volume":"15","author":"Shaw","year":"2025","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.neucom.2016.03.088","article-title":"Multi-label maximum entropy model for social emotion classification over short text","volume":"210","author":"Li","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.neucom.2018.08.053","article-title":"Applying multi-label techniques in emotion identification of short texts","volume":"320","author":"Almeida","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, H., and Xia, R. (2018, January 26\u201330). Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification. Proceedings of the Natural Language Processing and Chinese Computing, Hohhot, China.","DOI":"10.1007\/978-3-319-99495-6_21"},{"key":"ref_17","unstructured":"Huang, C., Trabelsi, A., Qin, X., Farruque, N., and Zaiane, O.R. (2019). Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jabreel, M., and Moreno, A. (2019). A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets. Appl. Sci., 9.","DOI":"10.3390\/app9061123"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ameer, I., Ashraf, N., Sidorov, G., and G\u00f3mez-Adorno, H. (2020). Multi-label Emotion Classification using Content-Based Features in Twitter. Comput. Y Sist., 24.","DOI":"10.13053\/cys-24-3-3476"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sarbazi-Azad, S., Akbari, A., and Khazeni, M. (2021, January 28\u201329). ExaAEC: A New Multi-label Emotion Classification Corpus in Arabic Tweets. Proceedings of the 2021 11th International Conference on Computer Engineering and Knowledge (ICCKE), Mashhad, Iran.","DOI":"10.1109\/ICCKE54056.2021.9721493"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e896","DOI":"10.7717\/peerj-cs.896","article-title":"Multi-label emotion classification of Urdu tweets using machine learning and deep learning techniques","volume":"8","author":"Ashraf","year":"2022","journal-title":"PeerJ Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1057\/s41599-023-01816-6","article-title":"Emotion classification for short texts: An improved multi-label method","volume":"10","author":"Liu","year":"2023","journal-title":"Humanit. Soc. Sci. Commun."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s11063-024-11500-8","article-title":"A Hybrid Model Based on Convolutional Neural Network and Long Short-Term Memory for Multi-label Text Classification","volume":"56","author":"Maragheh","year":"2024","journal-title":"Neural Process. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ding, F., Kang, X., Nishide, S., Guan, Z., and Ren, F. (2020, January 1\u201310). A fusion model for multi-label emotion classification based on BERT and topic clustering. Proceedings of the International Symposium on Artificial Intelligence and Robotics, Kitakyushu, Japan.","DOI":"10.1117\/12.2579255"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1839","DOI":"10.1109\/TASLP.2020.3001390","article-title":"Topic-Enhanced Capsule Network for Multi-Label Emotion Classification","volume":"28","author":"Fei","year":"2020","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_26","first-page":"7692","article-title":"Latent Emotion Memory for Multi-Label Emotion Classification","volume":"34","author":"Fei","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5789","DOI":"10.1007\/s10462-021-09958-2","article-title":"Transformer models for text-based emotion detection: A review of BERT-based approaches","volume":"54","author":"Acheampong","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ahanin, Z., Ismail, M.A., Singh, N.S.S., and AL-Ashmori, A. (2023). Hybrid Feature Extraction for Multi-Label Emotion Classification in English Text Messages. Sustainability, 15.","DOI":"10.3390\/su151612539"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"118534","DOI":"10.1016\/j.eswa.2022.118534","article-title":"Multi-label emotion classification in texts using transfer learning","volume":"213","author":"Ameer","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dong, X., Chen, X., Li, Y., Liu, J., Du, Y., and Li, X. (2024, January 20\u201322). Mutual Attention Network for Multi-label Emotion Recognition with Graph-Structured Label Representations. Proceedings of the 2024 International Conference on Ubiquitous Computing and Communications (IUCC), Chengdu, China.","DOI":"10.1109\/IUCC65928.2024.00040"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"15902","DOI":"10.1109\/ACCESS.2024.3354705","article-title":"Pseudo-Labeling with Large Language Models for Multi-Label Emotion Classification of French Tweets","volume":"12","author":"Malik","year":"2024","journal-title":"IEEE Access"},{"key":"ref_32","unstructured":"Siddiqui, M.H.F., Inkpen, D., and Gelbukh, A.F. (2024, January 27\u201331). Instruction Tuning of LLMs for Multi-label Emotion Classification in Social Media Content. Proceedings of the Canadian AI, Guelph, ON, Canada."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13388-014-0007-3","article-title":"Emotion classification of social media posts for estimating people\u2019s reactions to communicated alert messages during crises","volume":"3","author":"Brynielsson","year":"2014","journal-title":"Secur. Inform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2909","DOI":"10.1007\/s00146-024-02044-5","article-title":"Identifying emotions in earthquake tweets","volume":"40","author":"Anthony","year":"2025","journal-title":"AI Soc."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, C., and Hu, D. (2025). Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework. Appl. Sci., 15.","DOI":"10.3390\/app15084330"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2348668","DOI":"10.1080\/17538947.2024.2348668","article-title":"Multi-class multi-label classification of social media texts for typhoon damage assessment: A two-stage model fully integrating the outputs of the hidden layers of BERT","volume":"17","author":"Zou","year":"2024","journal-title":"Int. J. Digit. Earth"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Stojanovski, D., Strezoski, G., Madjarov, G., and Dimitrovski, I. (2015, January 1\u20133). Emotion identification in FIFA world cup tweets using convolutional neural network. Proceedings of the 2015 11th International Conference on Innovations in Information Technology (IIT), Dubai, United Arab Emirates.","DOI":"10.1109\/INNOVATIONS.2015.7381514"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fagbola, T.M., Abayomi, A., Mutanga, M.B., and Jugoo, V.R. (2022, January 15\u201317). Lexicon-Based Sentiment Analysis and Emotion Classification of Climate Change Related Tweets. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021), Online.","DOI":"10.1007\/978-3-030-96302-6_60"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Deniz, E., Erbay, H., and Cosar, M. (2022). Multi-Label Classification of E-Commerce Customer Reviews via Machine Learning. Axioms, 11.","DOI":"10.3390\/axioms11090436"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1007\/s13278-023-01132-6","article-title":"HatEmoTweet: Low-level emotion classifications and spatiotemporal trends of hate and offensive COVID-19 tweets","volume":"13","author":"Adesokan","year":"2023","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Cabral, R.C., Han, S.C., Poon, J., and Nenadic, G. (2024). MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media. Robotics, 13.","DOI":"10.3390\/robotics13030053"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Farasalsabila, F., Utami, E., and Raharjo, S. (2024, January 12\u201313). Multi-Label Classification using BERT for Cyberbullying Detection. Proceedings of the 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA), Balikpapan, Indonesia.","DOI":"10.1109\/ICSINTESA62455.2024.10748045"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"135","DOI":"10.7592\/FEJF2021.82.meder","article-title":"Online Coping with the First Wave: Covid Humor and Rumor on Dutch Social Media (March\u2013July 2020)","volume":"82","author":"Meder","year":"2021","journal-title":"Folk.-Electron. J. Folk."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"16883","DOI":"10.1109\/ACCESS.2022.3150329","article-title":"Investigating the Emotional Response to COVID-19 News on Twitter: A Topic Modelling and Emotion Classification Approach","volume":"10","author":"Oliveira","year":"2022","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"49882","DOI":"10.1109\/ACCESS.2023.3277868","article-title":"Text Mining and Emotion Classification on Monkeypox Twitter Dataset: A Deep Learning-Natural Language Processing (NLP) Approach","volume":"11","author":"Olusegun","year":"2023","journal-title":"IEEE Access"},{"key":"ref_46","unstructured":"He, P., Gao, J., and Chen, W. (2023). DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1177\/053901882021004003","article-title":"A psychoevolutionary theory of emotions","volume":"21","author":"Plutchik","year":"1982","journal-title":"Soc. Sci. Inf."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.neuroscience.2011.11.007","article-title":"Neural correlates of disgust- and fear-conditioned responses","volume":"201","author":"Klucken","year":"2012","journal-title":"Neuroscience"}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/4\/114\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T08:55:05Z","timestamp":1761123305000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/4\/114"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,22]]},"references-count":48,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["informatics12040114"],"URL":"https:\/\/doi.org\/10.3390\/informatics12040114","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,22]]}}}