{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T12:35:24Z","timestamp":1759581324420,"version":"3.41.0"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T00:00:00Z","timestamp":1742688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>\n            Sarcasm identification in textual data is the most captivating area of research in the current research trends. It is a challenging task for humans as well as for the computer. In this article, we have tried to identify sarcasm in the Hindi newspaper headlines of two of the most-read Hindi newspapers in India, namely Hindustan and Dainik Jagran. Initially, we collected 88,518 Hindi newspaper headlines and identified 1,945 headlines to be sarcastic, which we have considered for the present study. The headlines taken into consideration belong to the political domain and were published during some of the recent Legislative Assembly Elections of 2020, 2021, and 2022. Various machine learning and deep learning techniques have been used to develop the baseline models. It justifies the assumption that sarcastic text does not always bear a negative sentiment. It may bear a positive sentiment depending on the context. The present article aims at the creation of a dataset consisting of 1,945 Hindi newspaper headlines, training and testing machine learning and deep learning models, namely Extra Trees Classifier, Random Forest Classifier, XGBClassifier, fasttext-stackedTCN, and mBERT-stackedTCN for sarcasm identification on the dataset and comparing the results obtained by the models after the experiment. Out of all the choosen models, the Random Forest Classifier performs better with\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(F_1\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            score of 92.11 before data augmentation and 90.68 after data augmentation.\n          <\/jats:p>","DOI":"10.1145\/3714469","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T11:38:41Z","timestamp":1737718721000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Sarcasm Identification and Classification in Hindi Newspaper Headlines"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5781-6918","authenticated-orcid":false,"given":"Iram Ali","family":"Ahmad","sequence":"first","affiliation":[{"name":"Department of Linguistics, Faculty of Arts, Banaras Hindu University, Varanasi, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8042-8685","authenticated-orcid":false,"given":"Praveen","family":"Gatla","sequence":"additional","affiliation":[{"name":"Department of Linguistics, Faculty of Arts, Banaras Hindu University, Varanasi, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0096-2440","authenticated-orcid":false,"given":"Rajesh Kumar","family":"Mundotiya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, IIT Bhilai, Bhilai, India"}]}],"member":"320","published-online":{"date-parts":[[2025,3,23]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","unstructured":"Amirhossein Abaskohi et\u00a0al. 2022. 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Shobhit University Meerut.http:\/\/hdl.handle.net\/10603\/255748."},{"key":"e_1_3_3_6_2","article-title":"Sarcasm as a contradiction between a tweet and its temporal facts: A pattern-based approach","volume":"7","author":"Bharti Santosh Kumar","year":"2018","unstructured":"Santosh Kumar Bharti and Korra Sathya 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, 5 (2018), 67\u201379.","journal-title":"International Journal on Natural Language Computing (IJNLC)"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICAPR.2017.8593198"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00051"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1177\/001316446002000104"},{"key":"e_1_3_3_10_2","first-page":"4171","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. 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