{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:01:07Z","timestamp":1743019267176,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":41,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819702923"},{"type":"electronic","value":"9789819702930"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-0293-0_18","type":"book-chapter","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T13:02:24Z","timestamp":1714136544000},"page":"237-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sarcasm Detection in Newspaper Headlines"],"prefix":"10.1007","author":[{"given":"Vishnu Sai Reddy","family":"Chilpuri","sequence":"first","affiliation":[]},{"given":"Saaman","family":"Nadeem","sequence":"additional","affiliation":[]},{"given":"Tahir","family":"Mehmood","sequence":"additional","affiliation":[]},{"given":"Muhammad","family":"Yaqoob","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,27]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Abulaish M, Kamal A (2018) Self-deprecating sarcasm detection: an amalgamation of rule-based and machine learning approach. In: 2018 IEEE\/WIC\/ACM international conference on Web Intelligence (WI), pp 574\u2013579. IEEE","DOI":"10.1109\/WI.2018.00-35"},{"key":"18_CR2","unstructured":"Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau RJ (2011) Sentiment analysis of twitter data. In: Proceedings of the workshop on language in social media (LSM 2011), pp 30\u201338"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Belgiu M, Dragut L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Rem Sens 114:24\u201331","DOI":"10.1016\/j.isprsjprs.2016.01.011"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Chaudhari P, Chandankhede C (2017) Literature survey of sarcasm detection. In: 2017 International conference on wireless communications, signal processing and networking (WiSPNET), pp 2041\u20132046. IEEE","DOI":"10.1109\/WiSPNET.2017.8300120"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Christian H, Agus MP, Suhartono D (2016) Single document automatic text summarization using term frequency-inverse document frequency (tf-idf). ComTech: Comput Math Eng Appl 7(4):285\u2013294","DOI":"10.21512\/comtech.v7i4.3746"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Gen\u00e7 R (2017) The importance of communication in sustainability & sustainable strategies. Procedia Manufact 8:511\u2013516","DOI":"10.1016\/j.promfg.2017.02.065"},{"issue":"1","key":"18_CR7","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1108\/WJE-02-2021-0108","volume":"19","author":"J Godara","year":"2022","unstructured":"Godara J, Aron R, Shabaz M (2022) Sentiment analysis and sarcasm detection from social network to train health-care professionals. World J Eng 19(1):124\u2013133","journal-title":"World J Eng"},{"issue":"5\u20136","key":"18_CR8","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","volume":"18","author":"A Graves","year":"2005","unstructured":"Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural Netw 18(5\u20136):602\u2013610","journal-title":"Neural Netw"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Graves A, Graves A (2012) Long short-term memory. In: Supervised sequence labelling with recurrent neural networks, pp 37\u201345","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"18_CR10","unstructured":"Habert B, Adda G, Adda-Decker M, de Mare\u00fcil PB, Ferrari S, Ferret O, Illouz G, Paraubeck P (1998) Towards tokenization evaluation. LREC, pp 427\u2013432"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Handler A, Denny M, Wallach H, O\u2019Connor B (2016) Bag of what? simple noun phrase extraction for text analysis. In: Proceedings of the first workshop on NLP and computational social science, pp 114\u2013124","DOI":"10.18653\/v1\/W16-5615"},{"key":"18_CR12","unstructured":"Jain S, Ranjan A, Baviskar D (2018) Sarcasm detection in amazon product reviews. Int J Comput Sci Inform Technol 9(3)"},{"key":"18_CR13","doi-asserted-by":"publisher","first-page":"e645","DOI":"10.7717\/peerj-cs.645","volume":"7","author":"R Jamil","year":"2021","unstructured":"Jamil R, Ashraf I, Rustam F, Saad E, Mehmood A, Choi GS (2021) Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model. Peer J Comput Sci 7:e645","journal-title":"Peer J Comput Sci"},{"issue":"5","key":"18_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3124420","volume":"50","author":"A Joshi","year":"2017","unstructured":"Joshi A, Bhattacharyya P, Carman MJ (2017) Automatic sarcasm detection: a survey. ACM Comput Surv (CSUR) 50(5):1\u201322","journal-title":"ACM Comput Surv (CSUR)"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Kumar R, Bhat A (2021) An analysis on sarcasm detection over twitter during covid19. In: 2021 2nd international conference for emerging technology (INCET), pp 1\u20136. IEEE","DOI":"10.1109\/INCET51464.2021.9456392"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Lin R (2022) Comment texts sentiment analysis based on improved bi-LSTM and Naive Bayes. In: 2022 international conference on data analytics, computing and artificial intelligence (ICDACAI), pp 407\u2013412. IEEE","DOI":"10.1109\/ICDACAI57211.2022.00087"},{"key":"18_CR17","unstructured":"Maynard DG, Greenwood MA (2014) Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. In: LREC 2014 proceedings. ELRA"},{"issue":"5","key":"18_CR18","doi-asserted-by":"publisher","first-page":"255","DOI":"10.3390\/info14050255","volume":"14","author":"T Mehmood","year":"2023","unstructured":"Mehmood T, Gerevini AE, Lavelli A, Olivato M, Serina I (2023) Distilling knowledge with a teacher\u2019s multitask model for biomedical named entity recognition. Information 14(5):255","journal-title":"Information"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Mehmood T, Gerevini A, Lavelli A, Serina I (2019) Leveraging multi-task learning for biomedical named entity recognition. In: AI*IA 2019\u2014advances in artificial intelligence\u2014XVIIIth international conference of the Italian Association for Artificial Intelligence, Rende, Italy, 19\u201322 Nov 2019, Proceedings. Lecture notes in computer science, vol 11946, pp 431\u2013444. Springer, Berlin","DOI":"10.1007\/978-3-030-35166-3_31"},{"key":"18_CR20","unstructured":"Mehmood T, Gerevini A, Lavelli A, Serina I (2019) Multi-task learning applied to biomedical named entity recognition task. In: Proceedings of the sixth italian conference on computational linguistics, Bari, Italy, 13\u201315 Nov 2019. CEUR Workshop Proceedings, vol 2481. CEUR-WS.org"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Mehmood T, Gerevini AE, Lavelli A, Serina I (2020) Combining multi-task learning with transfer learning for biomedical named entity recognition. In: Knowledge based and intelligent information & engineering systems: proceedings of the 24th international conference KES-2020, Virtual Event, 16\u201318 Sept 2020. Procedia Computer Science, vol 176, pp 848\u2013857. Elsevier","DOI":"10.1016\/j.procs.2020.09.080"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Mehmood T, Md Rais HB (2016) Machine learning algorithms in context of intrusion detection. In: 2016 3rd international conference on computer and information sciences (ICCOINS), pp 369\u2013373","DOI":"10.1109\/ICCOINS.2016.7783243"},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Mehmood T, Serina I, Lavelli A, Putelli L, Gerevini A (2023) On the use of knowledge transfer techniques for biomedical named entity recognition. Fut Internet 15(2):79","DOI":"10.3390\/fi15020079"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Mishra A, Kanojia D, Nagar S, Dey K, Bhattacharyya P (2017) Harnessing cognitive features for sarcasm detection. arXiv preprint arXiv:1701.05574","DOI":"10.18653\/v1\/P16-1104"},{"key":"18_CR25","unstructured":"Misra R (2022) News headlines dataset for sarcasm detection. arXiv preprint arXiv:2212.06035"},{"key":"18_CR26","doi-asserted-by":"crossref","unstructured":"Mouthami K, Devi KN, Bhaskaran VM (2013) Sentiment analysis and classification based on textual reviews. In: 2013 international conference on Information communication and embedded systems (ICICES). pp 271\u2013276. IEEE","DOI":"10.1109\/ICICES.2013.6508366"},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"Ortigosa A, Mart\u00edn JM, Carro RM (2014) Sentiment analysis in facebook and its application to e-learning. Comput Hum Behav 31:527\u2013541","DOI":"10.1016\/j.chb.2013.05.024"},{"key":"18_CR28","doi-asserted-by":"crossref","unstructured":"Pawar N, Bhingarkar S (2020) Machine learning based sarcasm detection on twitter data. In: 2020 5th international conference on communication and electronics systems (ICCES), pp 957\u2013961. IEEE","DOI":"10.1109\/ICCES48766.2020.9137924"},{"key":"18_CR29","doi-asserted-by":"crossref","unstructured":"Pini M, Scalvini A, Liaqat MU, Ranzi R, Serina I, Mehmood T (2020) Evaluation of machine learning techniques for inflow prediction in Lake Como, Italy. In: Knowledge-based and intelligent information & engineering systems: proceedings of the 24th international conference KES-2020, Virtual Event, 16\u201318 Sept 2020. Procedia Computer Science, vol 176, pp 918\u2013927. Elsevier","DOI":"10.1016\/j.procs.2020.09.087"},{"key":"18_CR30","unstructured":"Plisson J, Lavrac N, Mladenic D et al (2004) A rule based approach to word lemmatization. In: Proceedings of IS, vol 3, pp 83\u201386"},{"key":"18_CR31","doi-asserted-by":"crossref","unstructured":"Porwal S, Ostwal G, Phadtare A, Pandey M, Marathe MV (2018) Sarcasm detection using recurrent neural network. In: 2018 second international conference on intelligent computing and control systems (ICICCS), pp 746\u2013748. IEEE","DOI":"10.1109\/ICCONS.2018.8663147"},{"issue":"9","key":"18_CR32","first-page":"2577","volume":"12","author":"V Preethi","year":"2021","unstructured":"Preethi V et al (2021) Survey on text transformation using bi-LSTM in natural language processing with text data. Turkish J Comput Math Educ (TURCOMAT) 12(9):2577\u20132585","journal-title":"Turkish J Comput Math Educ (TURCOMAT)"},{"key":"18_CR33","doi-asserted-by":"crossref","unstructured":"Prokhorov S, Safronov V (2019) AI for AI: what NLP techniques help researchers find the right articles on NLP. In: 2019 international conference on artificial intelligence: applications and innovations (IC-AIAI), pp 76\u2013765. IEEE","DOI":"10.1109\/IC-AIAI48757.2019.00023"},{"key":"18_CR34","doi-asserted-by":"crossref","unstructured":"Rajadesingan A, Zafarani R, Liu H (2015) Sarcasm detection on twitter: a behavioral modeling approach. In: Proceedings of the eighth ACM international conference on web search and data mining (pp. 97\u2013106)","DOI":"10.1145\/2684822.2685316"},{"key":"18_CR35","doi-asserted-by":"crossref","unstructured":"Runeson P, Alexandersson M, Nyholm O (2007) Detection of duplicate defect reports using natural language processing. In: 29th international conference on software engineering (ICSE\u201907), pp 499\u2013510. IEEE","DOI":"10.1109\/ICSE.2007.32"},{"issue":"5","key":"18_CR36","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1177\/1470785320921779","volume":"62","author":"SM Sarsam","year":"2020","unstructured":"Sarsam SM, Al-Samarraie H, Alzahrani AI, Wright B (2020) Sarcasm detection using machine learning algorithms in twitter: a systematic review. Int J Mark Res 62(5):578\u2013598","journal-title":"Int J Mark Res"},{"key":"18_CR37","doi-asserted-by":"crossref","unstructured":"Shrikhande P, Setty V, Sahani A (2020) Sarcasm detection in newspaper headlines. In: 2020 IEEE 15th international conference on industrial and information systems (ICIIS), pp 483\u2013487. IEEE","DOI":"10.1109\/ICIIS51140.2020.9342742"},{"key":"18_CR38","unstructured":"Staudemeyer RC, Morris ER (2019) Understanding LSTM\u2013a tutorial into long shortterm memory recurrent neural networks. arXiv preprint arXiv:1909.09586"},{"issue":"3","key":"18_CR39","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/TGE.1977.6498972","volume":"15","author":"PH Swain","year":"1977","unstructured":"Swain PH, Hauska H (1977) The decision tree classifier: design and potential. IEEE Trans Geosci Electron 15(3):142\u2013147","journal-title":"IEEE Trans Geosci Electron"},{"issue":"2","key":"18_CR40","first-page":"612","volume":"11","author":"S Tangirala","year":"2020","unstructured":"Tangirala S (2020) Evaluating the impact of gini index and information gain on classification using decision tree classifier algorithm. Int J Adv Comput Sci Appl 11(2):612\u2013619","journal-title":"Int J Adv Comput Sci Appl"},{"key":"18_CR41","unstructured":"Wol Kowicz J, Kulka Z, Keselj V (2008) N-gram-based approach to composer recognition. Arch Acoust 33(1):43\u201355 (2008)"}],"container-title":["Lecture Notes on Data Engineering and Communications Technologies","Data Science and Emerging Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-0293-0_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T13:07:48Z","timestamp":1714136868000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-0293-0_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819702923","9789819702930"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-0293-0_18","relation":{},"ISSN":["2367-4512","2367-4520"],"issn-type":[{"type":"print","value":"2367-4512"},{"type":"electronic","value":"2367-4520"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"27 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DaSET","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The International Conference on Data Science and Emerging Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"daset2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icdaset.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}