Sarcasm target identification with LSTM networks

Bölücü, Necva; Can, Burcu

dc.contributor.author	Bölücü, Necva
dc.contributor.author	Can, Burcu
dc.date.accessioned	2020-10-30T11:05:10Z
dc.date.available	2020-10-30T11:05:10Z
dc.date.issued	2021-01-07
dc.identifier.citation	Bölücü, N. and Can, B. (2020) Sarcasm target identification with LSTM networks, 28th IEEE 13th Signal Processing and Communications Applications Conference (SIU), 5th-7th October, 2020, Online.	en
dc.identifier.issn	2165-0608	en
dc.identifier.doi	10.1109/SIU49456.2020.9302321
dc.identifier.uri	http://hdl.handle.net/2436/623745
dc.description	This is an accepted manuscript of an article published by IEEE in 28th IEEE Conference on Signal Processing and Communications Applications (SIU) (2020), available online at: https://ieeexplore.ieee.org/document/9302321 The accepted version of the publication may differ from the final published version.	en
dc.description.abstract	Geçmi¸s yıllarda, kinayeli metinler üzerine yapılan çalı¸smalarda temel hedef metinlerin kinaye içerip içermediginin ˘ tespit edilmesiydi. Sosyal medya kullanımı ile birlikte siber zorbalıgın yaygınla¸sması, metinlerin sadece kinaye içerip içer- ˘ mediginin tespit edilmesinin yanısıra kinayeli metindeki hedefin ˘ belirlenmesini de gerekli kılmaya ba¸slamı¸stır. Bu çalı¸smada, kinayeli metinlerde hedef tespiti için bir derin ögrenme modeli ˘ kullanılarak hedef tespiti yapılmı¸s ve elde edilen sonuçlar literatürdeki ˙Ingilizce üzerine olan benzer çalı¸smalarla kıyaslanmı¸stır. Sonuçlar, önerdigimiz modelin kinaye hedef tespitinde benzer ˘ çalı¸smalara göre daha iyi çalı¸stıgını göstermektedir. The earlier work on sarcastic texts mainly concentrated on detecting the sarcasm on a given text. With the spread of cyber-bullying with the use of social media, it becomes also essential to identify the target of the sarcasm besides detecting the sarcasm. In this study, we propose a deep learning model for target identification on sarcastic texts and compare it with other work on English. The results show that our model outperforms the related work on sarcasm target identification.	en
dc.format	application/pdf	en
dc.language.iso	other	en
dc.publisher	IEEE	en
dc.relation.url	http://siu2020.medipol.edu.tr/en/	en
dc.subject	natural language processing	en
dc.subject	sarcasm target identification	en
dc.subject	recurrent neural networks	en
dc.subject	deep learning	en
dc.title	Sarcasm target identification with LSTM networks	en
dc.type	Conference contribution	en
dc.date.updated	2020-10-23T16:33:11Z
dc.conference.name	28th IEEE Conference on Signal Processing and Communications Applications
dc.conference.location	Virtual
pubs.finish-date	2020-10-07
pubs.start-date	2020-10-05
dc.date.accepted	2020-02-20
rioxxterms.funder	University of Wolverhampton	en
rioxxterms.identifier.project	UOW30102020BC	en
rioxxterms.version	AM	en
rioxxterms.licenseref.uri	https://creativecommons.org/licenses/by-nc-nd/4.0/	en
rioxxterms.licenseref.startdate	2021-01-07	en
refterms.dateFCD	2020-10-30T11:00:27Z
refterms.versionFCD	AM