{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T23:54:33Z","timestamp":1784332473272,"version":"3.55.0"},"reference-count":53,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2023,5,31]]},"abstract":"<jats:p>Automated sarcasm detection is deemed as a complex natural language processing task and extending it to a morphologically-rich and free-order dominant indigenous Indian language Hindi is another challenge in itself. The scarcity of resources and tools such as annotated corpora, lexicons, dependency parser, Part-of-Speech tagger, and benchmark datasets engorge the linguistic challenges of sarcasm detection in low-resource languages like Hindi. Furthermore, as context incongruity is imperative to detect sarcasm, various linguistic, aural and visual cues can be used to predict target utterance as sarcastic. While pre-trained word embeddings capture the meanings, semantic relationships and different types of contexts in the form of word representations, emojis can also render useful contextual information, analogous to human facial expressions, for gauging sarcasm. Thus, the goal of this research is to demonstrate the use of a hybrid deep learning model trained using two embeddings, namely word and emoji embeddings to detect sarcasm. The model is validated on a Hindi tweets dataset, Sarc-H, manually annotated with sarcastic and non-sarcastic labels. The preliminary results clearly depict the importance of using emojis for sarcasm detection, with our model attaining an accuracy of 97.35% with an F-score of 0.9708. The research validates that automated feature engineering facilitates efficient and repeatable predictive model for detecting sarcasm in indigenous, low-resource languages.<\/jats:p>","DOI":"10.1145\/3519299","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T12:14:36Z","timestamp":1660133676000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Hybrid Deep Learning Model for Sarcasm Detection in Indian Indigenous Language Using Word-Emoji Embeddings"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4263-7168","authenticated-orcid":false,"given":"Akshi","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Information Technology, Netaji Subhas University of Technology, Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0832-0362","authenticated-orcid":false,"given":"Saurabh Raj","family":"Sangwan","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Engineering, Netaji Subhas University of Technology, Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8892-9444","authenticated-orcid":false,"given":"Adarsh Kumar","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Delhi Technological University, Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6903-4194","authenticated-orcid":false,"given":"Gandharv","family":"Wadhwa","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Delhi Technological University, Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/mis.2013.30"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.102141"},{"key":"e_1_3_2_4_2","article-title":"Contextual semantics using hierarchical attention network for sentiment classification in social internet-of-things","author":"Kumar A.","year":"2021","unstructured":"A. Kumar. 2021. Contextual semantics using hierarchical attention network for sentiment classification in social internet-of-things. Multimed. Tools Appl. https:\/\/doi.org\/10.1007\/s11042-021-11262-8","journal-title":"Multimed. Tools Appl."},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106198"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2019.2954667"},{"key":"e_1_3_2_7_2","doi-asserted-by":"crossref","unstructured":"S. Poria I. Chaturvedi E. Cambria and A. Hussain. 2016. Convolutional MKL based multimodal emotion recognition and sentiment analysis. In 2016 IEEE 16th International Conference on Data Mining (ICDM) . IEEE 439\u2013448.","DOI":"10.1109\/ICDM.2016.0055"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2019.2904691"},{"key":"e_1_3_2_9_2","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.18653\/v1\/D15-1116","volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","author":"Ghosh D.","year":"2015","unstructured":"D. Ghosh, W. Guo, and S. Muresan. 2015. Sarcastic or not: Word embeddings to predict the literal or sarcastic meaning of words. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 1003\u20131012."},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/2939334"},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/978-981-15-1451-7_44","volume-title":"Cognitive Informatics and Soft Computing","author":"Kumar A.","year":"2020","unstructured":"A. Kumar and G. Garg. 2020. The multifaceted concept of context in sentiment analysis. In Cognitive Informatics and Soft Computing. Springer, Singapore, 413\u2013421."},{"key":"e_1_3_2_12_2","unstructured":"N. L. Bliss-Carroll. 2016. The nature function and value of emojis as contemporary tools of digital interpersonal communication."},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/2808797.2808910"},{"key":"e_1_3_2_14_2","first-page":"1","article-title":"Empirical study of shallow and deep learning models for sarcasm detection using context in benchmark datasets","author":"Kumar A.","year":"2019","unstructured":"A. Kumar and G. Garg. 2019. Empirical study of shallow and deep learning models for sarcasm detection using context in benchmark datasets. Journal of Ambient Intelligence and Humanized Computing (2019), 1\u201316.","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.5555\/1870568.1870582"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2016.01.015"},{"issue":"5","key":"e_1_3_2_17_2","doi-asserted-by":"crossref","first-page":"Article 90","DOI":"10.1145\/3461764","article-title":"Sentiment analysis using XLM-R transformer and zero-shot transfer learning on resource-poor Indian language","volume":"20","author":"Kumar A.","year":"2021","unstructured":"A. Kumar and V. H. C. Albuquerque. 2021. Sentiment analysis using XLM-R transformer and zero-shot transfer learning on resource-poor Indian language. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 20, 5, Article 90 (September 2021), 13 pages. DOI:https:\/\/doi.org\/10.1145\/3461764","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inf. Process."},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/978-3-319-69900-4_86","volume-title":"International Conference on Pattern Recognition and Machine Intelligence","author":"Bharti S. K.","year":"2017","unstructured":"S. K. Bharti, K. S. Babu, and S. K. Jena. 2017. Harnessing online news for sarcasm detection in Hindi tweets. In International Conference on Pattern Recognition and Machine Intelligence. Springer, Cham. 679\u2013686."},{"issue":"7","key":"e_1_3_2_19_2","first-page":"8","article-title":"Sarcasm detection in Hindi sentences using support vector machine","volume":"4","author":"Desai N.","year":"2016","unstructured":"N. Desai and A. D. Dave. 2016. Sarcasm detection in Hindi sentences using support vector machine. International Journal 4, 7 (2016), 8\u201315.","journal-title":"International Journal"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-08904-8"},{"key":"e_1_3_2_21_2","first-page":"1","article-title":"D-BullyRumbler: A safety rumble strip to resolve online denigration bullying using a hybrid filter-wrapper approach","author":"Sangwan S. R.","year":"2020","unstructured":"S. R. Sangwan and M. P. S. Bhatia. 2020. D-BullyRumbler: A safety rumble strip to resolve online denigration bullying using a hybrid filter-wrapper approach. Multimedia Systems. 1\u201317.","journal-title":"Multimedia Systems"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.2174\/2213275912666190409152308"},{"key":"e_1_3_2_23_2","first-page":"1","article-title":"Multi-input integrative learning using deep neural networks and transfer learning for cyberbullying detection in real-time code-mix data","author":"Kumar A.","year":"2020","unstructured":"A. Kumar and N. Sachdeva. 2020. Multi-input integrative learning using deep neural networks and transfer learning for cyberbullying detection in real-time code-mix data. Multimedia Systems. 1\u201315.","journal-title":"Multimedia Systems"},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","unstructured":"B. Eisner T. Rockt\u00e4schel I. Augenstein M. Bo\u0161njak and S. Riedel. 2016. emoji2vec: Learning emoji representations from their description. arXiv preprint arXiv:1609.08359.","DOI":"10.18653\/v1\/W16-6208"},{"key":"e_1_3_2_25_2","article-title":"Recent advances in convolutional neural networks","volume":"1","author":"Gu J.","year":"2018","unstructured":"J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, and T. Chen. 2018. Recent advances in convolutional neural networks. Pattern Recognition 1 (2018 May), 77:354\u201377.","journal-title":"Pattern Recognition"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_27_2","volume-title":"Ninth International Conference on Spoken Language Processing","author":"Tepperman J.","year":"2006","unstructured":"J. Tepperman, D. Traum, and S. Narayanan. 2006. \u201cYeah Right\u201d: Sarcasm recognition for spoken dialogue systems. In Ninth International Conference on Spoken Language Processing."},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.5555\/1611528.1611529"},{"key":"e_1_3_2_29_2","first-page":"581","volume-title":"Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies","author":"Gonz\u00e1lez-Ib\u00e1nez R.","year":"2011","unstructured":"R. Gonz\u00e1lez-Ib\u00e1nez, S. Muresan, and N. Wacholder. 2011. Identifying sarcasm in Twitter: A closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 581\u2013586."},{"key":"e_1_3_2_30_2","first-page":"704","volume-title":"Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing","author":"Riloff E.","year":"2013","unstructured":"E. Riloff, A. Qadir, P. Surve, L. De Silva, N. Gilbert, and R. Huang. 2013. Sarcasm as contrast between a positive sentiment and negative situation. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 704\u2013714."},{"key":"e_1_3_2_31_2","unstructured":"C. C. Liebrecht F. A. Kunneman and A. P. J. van Den Bosch. 2013. The perfect solution for detecting sarcasm in tweets# not."},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-2124"},{"key":"e_1_3_2_33_2","doi-asserted-by":"crossref","first-page":"146","DOI":"10.18653\/v1\/K16-1015","volume-title":"Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning","author":"Joshi A.","year":"2016","unstructured":"A. Joshi, V. Tripathi, P. Bhattacharyya, and M. Carman. 2016. Harnessing sequence labeling for sarcasm detection in dialogue from TV series \u2018Friends\u2019. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning. 146\u2013155."},{"key":"e_1_3_2_34_2","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1007\/978-3-030-30577-2_61","volume-title":"Proceedings of ICETIT 2019","author":"Kumar A.","year":"2020","unstructured":"A. Kumar and G. Garg. 2020. Sarcasm detection using feature-variant learning models. In Proceedings of ICETIT 2019. Springer, Cham. 683\u2013693."},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2964321"},{"key":"e_1_3_2_36_2","article-title":"Towards multimodal sarcasm detection (an _Obviously_ perfect paper)","author":"Castro S.","year":"2019","unstructured":"S. Castro, D. Hazarika, V. P\u00e9rez-Rosas, R. Zimmermann, R. Mihalcea, and S. Poria. 2019. Towards multimodal sarcasm detection (an _Obviously_ perfect paper). arXiv preprint arXiv:1906.01815.","journal-title":"arXiv preprint"},{"key":"e_1_3_2_37_2","volume-title":"International Conference on Advances in Engineering Science Management & Technology (ICAESMT)-2019","author":"Kumar A.","year":"2019","unstructured":"A. Kumar and G. Garg. 2019. Sarc-m: Sarcasm detection in typo-graphic memes. In International Conference on Advances in Engineering Science Management & Technology (ICAESMT)-2019, Uttaranchal University, Dehradun, India."},{"key":"e_1_3_2_38_2","article-title":"Are word embedding-based features useful for sarcasm detection","author":"Joshi A.","year":"2016","unstructured":"A. Joshi, V. Tripathi, K. Patel, P. Bhattacharyya, and M. Carman. 2016. Are word embedding-based features useful for sarcasm detection?. arXiv preprint arXiv:1610.00883.","journal-title":"arXiv preprint"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W16-0425"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3124420"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2899260"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-99740-7_12"},{"key":"e_1_3_2_43_2","first-page":"213","volume-title":"Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers","author":"Pt\u00e1\u010dek T.","year":"2014","unstructured":"T. Pt\u00e1\u010dek, I. Habernal, and J. Hong. 2014. Sarcasm detection on Czech and English Twitter. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. 213\u2013223."},{"key":"e_1_3_2_44_2","first-page":"459","volume-title":"International Conference on Web-Age Information Management","author":"Liu P.","year":"2014","unstructured":"P. Liu, W. Chen, G. Ou, T. Wang, D. Yang, and K. Lei. 2014. Sarcasm detection in social media based on imbalanced classification. In International Conference on Web-Age Information Management. Springer, Cham. 459\u2013471."},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-018-9578-5"},{"key":"e_1_3_2_46_2","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1109\/ICACSIS.2013.6761575","volume-title":"2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","author":"Lunando E.","year":"2013","unstructured":"E. Lunando and A. Purwarianti. 2013. Indonesian social media sentiment analysis with sarcasm detection. In 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE, 195\u2013198."},{"key":"e_1_3_2_47_2","unstructured":"S. Swami A. Khandelwal V. Singh S. S. Akhtar and M. Shrivastava. 2018. A corpus of English-Hindi code-mixed tweets for sarcasm detection. arXiv preprint arXiv:1805.11869."},{"key":"e_1_3_2_48_2","unstructured":"T. Mikolov I. Sutskever K. Chen G. Corrado and J. Dean. 2013. Distributed representations of words and phrases and their compositionality. arXiv preprint arXiv:1310.4546."},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","unstructured":"B. Felbo A. Mislove A. S\u00f8gaard I. Rahwan and S. Lehmann. 2017. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment emotion and sarcasm. arXiv preprint arXiv:1708.00524.","DOI":"10.18653\/v1\/D17-1169"},{"key":"e_1_3_2_50_2","first-page":"115","volume-title":"International Conference on Machine Learning","author":"Bergstra J.","year":"2013","unstructured":"J. Bergstra, D. Yamins, and D. Cox. 2013. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In International Conference on Machine Learning. PMLR, 115\u2013123."},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W19-4328"},{"key":"e_1_3_2_52_2","first-page":"7","article-title":"Sarcasm as a contradiction between a tweet and its temporal facts: A pattern-based approach","author":"Bharti S. K.","year":"2018","unstructured":"S. K. Bharti and K. S. Babu. 2018. Sarcasm as a contradiction between a tweet and its temporal facts: A pattern-based approach. International Journal on Natural Language Computing (IJNLC) 7 (2018).","journal-title":"International Journal on Natural Language Computing (IJNLC)"},{"key":"e_1_3_2_53_2","first-page":"2017","volume-title":"Ninth International Conference on Advances in Pattern Recognition (ICAPR)","author":"Bharti S. K.","year":"2017","unstructured":"S. K. Bharti, K. S. Babu, and R. Raman. 2017. Context-based sarcasm detection in Hindi tweets. In Ninth International Conference on Advances in Pattern Recognition (ICAPR). 2017."},{"issue":"1","key":"e_1_3_2_54_2","first-page":"43","article-title":"Performance evaluation of machine learning algorithms for detecting Hindi sarcasm","volume":"29","author":"Katyayan P.","year":"2021","unstructured":"P. Katyayan and N. Joshi. 2021. Performance evaluation of machine learning algorithms for detecting Hindi sarcasm. In 2021 Bharatiya Vaigyanik Evam Audyogik Anusandhan Patrika 29, 1 (2021), 43\u201348.","journal-title":"2021 Bharatiya Vaigyanik Evam Audyogik Anusandhan Patrika"}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3519299","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3519299","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:21Z","timestamp":1750191141000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3519299"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,8]]},"references-count":53,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,5,31]]}},"alternative-id":["10.1145\/3519299"],"URL":"https:\/\/doi.org\/10.1145\/3519299","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"value":"2375-4699","type":"print"},{"value":"2375-4702","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,8]]},"assertion":[{"value":"2021-10-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-02-15","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}