{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:41:40Z","timestamp":1787028100523,"version":"3.56.0"},"reference-count":33,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Sarcasm and irony represent intricate linguistic forms in social media communication, demanding nuanced comprehension of context and tone. In this study, we propose an advanced natural language processing methodology utilizing long short-term memory with an attention mechanism (LSTM-AM) to achieve an impressive accuracy of 99.86% in detecting and interpreting sarcasm and irony within social media text. Our approach involves innovating novel deep learning models adept at capturing subtle cues, contextual dependencies, and sentiment shifts inherent in sarcastic or ironic statements. Furthermore, we explore the potential of transfer learning from extensive language models and integrating multimodal information, such as emojis and images, to heighten the precision of sarcasm and irony detection. Rigorous evaluation against benchmark datasets and real-world social media content showcases the efficacy of our proposed models. The outcomes of this research hold paramount significance, offering a substantial advancement in comprehending intricate language nuances in digital communication. These findings carry profound implications for sentiment analysis, opinion mining, and an enhanced understanding of social media dynamics.<\/jats:p>","DOI":"10.3390\/computers12110231","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T02:20:37Z","timestamp":1699928437000},"page":"231","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Utilizing an Attention-Based LSTM Model for Detecting Sarcasm and Irony in Social Media"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7940-060X","authenticated-orcid":false,"given":"Deborah","family":"Olaniyan","sequence":"first","affiliation":[{"name":"Department of Computer Science, Landmark University, Omu-Aran 251103, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roseline Oluwaseun","family":"Ogundokun","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Landmark University, Omu-Aran 251103, Nigeria"},{"name":"Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olorunfemi Paul","family":"Bernard","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Auchi Polytechnic, Auchi 312101, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julius","family":"Olaniyan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Landmark University, Omu-Aran 251103, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2809-2213","authenticated-orcid":false,"given":"Rytis","family":"Maskeli\u016bnas","sequence":"additional","affiliation":[{"name":"Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0704-6143","authenticated-orcid":false,"given":"Hakeem Babalola","family":"Akande","sequence":"additional","affiliation":[{"name":"Department of Telecommunication Science, University of Ilorin, Ilorin 240003, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12","DOI":"10.22215\/timreview\/1117","article-title":"The fourth industrial revolution (Industry 4.0): A social innovation perspective","volume":"7","author":"Morrar","year":"2017","journal-title":"Technol. Innov. Manag. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1504\/IJNVO.2021.120171","article-title":"Students\u2019 perspective on online teaching in higher institutions during COVID-19 pandemic","volume":"25","author":"Ogundokun","year":"2021","journal-title":"Int. J. Netw. Virtual Organ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hui, A. (2019). A Theory of the Aphorism: From Confucius to Twitter, Princeton University Press.","DOI":"10.23943\/princeton\/9780691188959.001.0001"},{"key":"ref_4","first-page":"1874","article-title":"Semantics-based clustering approach for similar research area detection","volume":"18","author":"Adebiyi","year":"2020","journal-title":"Telecommun. Comput. Electron. Control"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fisman, R., and Golden, M.A. (2017). Corruption: What Everyone Needs to Know, Oxford University Press.","DOI":"10.1093\/wentk\/9780190463984.001.0001"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1080\/0267257X.2022.2105933","article-title":"More than words can say: A multimodal approach to understanding meaning and sentiment in social media","volume":"38","author":"Mehmet","year":"2022","journal-title":"J. Mark. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"116398","DOI":"10.1016\/j.eswa.2021.116398","article-title":"The unbearable hurtfulness of sarcasm","volume":"193","author":"Frenda","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_8","unstructured":"Strozzo, S.L. (2023). Comedic Sarcasm: Meaning Generation by Second Audience Teens. [Ph.D. Thesis, University of Leicester]."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Moseley, R. (2016). Keys to Play: Music as a Ludic Medium from Apollo to Nintendo, University of California Press.","DOI":"10.1525\/luminos.16"},{"key":"ref_10","unstructured":"Davidov, D., Tsur, O., and Rappoport, A. (2010, January 23\u201327). Enhanced sentiment learning using twitter hashtags and smileys. Proceedings of the 23rd International Conference on Computational Linguistics, Beijing, China."},{"key":"ref_11","unstructured":"Elhanashi, A., Gasmi, K., Begni, A., Dini, P., Zheng, Q., and Saponara, S. (2022). International Conference on Applications in Electronics Pervading Industry, Environment and Society, Proceedings of the ApplePies 2022: Applications in Electronics Pervading Industry, Environment and Society, Genoa, Italy, 26\u201327 September 2021, Springer Nature."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s10579-012-9196-x","article-title":"A multidimensional approach for detecting irony in twitter","volume":"47","author":"Reyes","year":"2013","journal-title":"Lang. Resour. Eval."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Abulaish, M., and Kamal, A. (2018, January 3\u20136). Self-deprecating sarcasm detection: An amalgamation of rule-based and machine learning approach. Proceedings of the 2018 IEEE\/WIC\/ACM International Conference on Web Intelligence (WI), Santiago, Chile.","DOI":"10.1109\/WI.2018.00-35"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sundararajan, K., and Palanisamy, A. (2020). Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter, Computational Intelligence and Neuroscience.","DOI":"10.1155\/2020\/2860479"},{"key":"ref_15","first-page":"012105","article-title":"Detection on sarcasm using machine learning classifiers and rule based approach","volume":"Volume 1055","author":"Sentamilselvan","year":"2021","journal-title":"Proceedings of the International Virtual Conference on Robotics, Automation, Intelligent Systems and Energy (IVC RAISE 2020)"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., and Dyer, C. (2016). Neural architectures for named entity recognition. arXiv.","DOI":"10.18653\/v1\/N16-1030"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mandal, P.K., and Mahto, R. (2019, January 1\u20133). Deep CNN-LSTM with word embeddings for news headline sarcasm detection. Proceedings of the 16th International Conference on Information Technology-New Generations (ITNG 2019), Las Vegas, NV, USA.","DOI":"10.1007\/978-3-030-14070-0_69"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"48501","DOI":"10.1109\/ACCESS.2021.3068323","article-title":"Context-based feature technique for sarcasm identification in benchmark datasets using deep learning and BERT model","volume":"9","author":"Eke","year":"2021","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tk\u00e1cov\u00e1, H., Pavl\u00edkov\u00e1, M., Stranovsk\u00e1, E., and Kr\u00e1lik, R. (2023). Individual (non) resilience of university students to digital media manipulation after COVID-19 (case study of Slovak initiatives). Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20021605"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tkacov\u00e1, H., Kr\u00e1lik, R., Tvrdo\u0148, M., Jenisov\u00e1, Z., and Martin, J.G. (2022). Credibility and Involvement of social media in education\u2014Recommendations for mitigating the negative effects of the pandemic among high school students. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19052767"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"503","DOI":"10.15503\/jecs2023.1.503.513","article-title":"Post-Covid Media Behaviour Patterns of the Generation Z Members in Slovakia","volume":"14","author":"Lenghart","year":"2023","journal-title":"J. Educ. Cult. Soc."},{"key":"ref_22","unstructured":"Shaila, S.G., Vinod, D., Dias, J., and Patra, S. (2022, January 22\u201323). Twitter Data-based Sarcastic Sentiment Analysis using Deep Learning Framework. Proceedings of the 2022 International Conference on Artificial Intelligence and Data Engineering (AIDE), Karkala, India."},{"key":"ref_23","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_24","unstructured":"Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (Preprint, 2018). Improving language understanding by generative pre-training, Preprint, work in progress."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Naseem, U., Razzak, I., Eklund, P., and Musial, K. (2020, January 19\u201324). Towards improved deep contextual embedding for the identification of irony and sarcasm. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9207237"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Babanejad, N., Davoudi, H., An, A., and Papagelis, M. (2020, January 8\u201313). Affective and contextual embedding for sarcasm detection. Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain.","DOI":"10.18653\/v1\/2020.coling-main.20"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s10844-022-00755-z","article-title":"BERT-LSTM model for sarcasm detection in code-mixed social media post","volume":"60","author":"Pandey","year":"2023","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Barbieri, F., Saggion, H., and Ronzano, F. (2014, January 27). Modelling sarcasm in twitter, a novel approach. Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Baltimore, MD, USA.","DOI":"10.3115\/v1\/W14-2609"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Potash, P., Ferguson, A., and Hazen, T.J. (2019, January 1). Ranking passages for argument convincingness. Proceedings of the 6th Workshop on Argument Mining, Florence, Italy.","DOI":"10.18653\/v1\/W19-4517"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Agarwal, A., Yadav, A., and Vishwakarma, D.K. (2019, January 29\u201331). Multimodal sentiment analysis via RNN variants. Proceedings of the 2019 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD), Honolulu, HI, USA.","DOI":"10.1109\/BCD.2019.8885108"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, K., Shen, W., Yang, Y., Quan, X., and Wang, R. (2020). Relational graph attention network for aspect-based sentiment analysis. arXiv.","DOI":"10.18653\/v1\/2020.acl-main.295"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.inffus.2020.08.006","article-title":"Quantum-inspired multimodal fusion for video sentiment analysis","volume":"65","author":"Li","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"16332","DOI":"10.1007\/s10489-022-03343-4","article-title":"ICDN: Integrating consistency and difference networks by transformer for multimodal sentiment analysis","volume":"53","author":"Zhang","year":"2023","journal-title":"Appl. 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