Exploring Author Context for Detecting Intended vs Perceived Sarcasm

Silviu Vlad Oprea; Walid Magdy

Exploring Author Context for Detecting Intended vs Perceived Sarcasm

Silviu Vlad Oprea, Walid Magdy

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

We investigate the impact of using author context on textual sarcasm detection. We deﬁne author context as the embedded representation of their historical posts on Twitter and suggest neural models that extract these representations. We experiment with two tweet datasets, one labelled manually for sarcasm, and the other via tag-based distant supervision. We achieve state-of-the-art performance on the second dataset, but not on the one labelled manually, indicating a difference between intended sarcasm, captured by distant supervision, and perceived sarcasm, captured by manual labelling.

Original language	English
Title of host publication	Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Editors	Anna Korhonen, David Traum, Lluís Màrquez
Place of Publication	Florence, Italy
Publisher	Association for Computational Linguistics (ACL)
Pages	2854–2859
Number of pages	6
Publication status	Published - 2 Aug 2019
Event	57th Annual Meeting of the Association for Computational Linguistics - Fortezza da Basso, Florence, Italy Duration: 28 Jul 2019 → 2 Aug 2019 Conference number: 57 http://www.acl2019.org/EN/index.xhtml

Conference

Conference	57th Annual Meeting of the Association for Computational Linguistics
Abbreviated title	ACL 2019
Country/Territory	Italy
City	Florence
Period	28/07/19 → 2/08/19
Internet address	http://www.acl2019.org/EN/index.xhtml

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Exploring Author Context_OPREA_dOa13052019_AFVAccepted author manuscript, 250 KB
Exploring Author Context_OPREA_DoA130519_VoR
© 2019 Association for Computational Linguistics
Final published version, 298 KBLicence: Creative Commons: Attribution (CC-BY)

https://www.aclweb.org/anthology/P19-1275Licence: Creative Commons: Attribution (CC-BY)

Cite this

@inproceedings{559167e010eb42fd9768fd347a771048,

title = "Exploring Author Context for Detecting Intended vs Perceived Sarcasm",

abstract = "We investigate the impact of using author context on textual sarcasm detection. We deﬁne author context as the embedded representation of their historical posts on Twitter and suggest neural models that extract these representations. We experiment with two tweet datasets, one labelled manually for sarcasm, and the other via tag-based distant supervision. We achieve state-of-the-art performance on the second dataset, but not on the one labelled manually, indicating a difference between intended sarcasm, captured by distant supervision, and perceived sarcasm, captured by manual labelling.",

author = "Oprea, {Silviu Vlad} and Walid Magdy",

year = "2019",

month = aug,

day = "2",

language = "English",

pages = "2854–2859",

editor = "Anna Korhonen and David Traum and Llu{\'i}s M{\`a}rquez",

booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics (ACL)",

note = "57th Annual Meeting of the Association for Computational Linguistics, ACL 2019 ; Conference date: 28-07-2019 Through 02-08-2019",

url = "http://www.acl2019.org/EN/index.xhtml",

}

Oprea, SV & Magdy, W 2019, Exploring Author Context for Detecting Intended vs Perceived Sarcasm. in A Korhonen, D Traum & L Màrquez (eds), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics., P19-1275, Association for Computational Linguistics (ACL), Florence, Italy, pp. 2854–2859, 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 28/07/19. <https://www.aclweb.org/anthology/P19-1275>

Exploring Author Context for Detecting Intended vs Perceived Sarcasm. / Oprea, Silviu Vlad; Magdy, Walid.
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. ed. / Anna Korhonen; David Traum; Lluís Màrquez. Florence, Italy: Association for Computational Linguistics (ACL), 2019. p. 2854–2859 P19-1275.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Exploring Author Context for Detecting Intended vs Perceived Sarcasm

AU - Oprea, Silviu Vlad

AU - Magdy, Walid

N1 - Conference code: 57

PY - 2019/8/2

Y1 - 2019/8/2

N2 - We investigate the impact of using author context on textual sarcasm detection. We deﬁne author context as the embedded representation of their historical posts on Twitter and suggest neural models that extract these representations. We experiment with two tweet datasets, one labelled manually for sarcasm, and the other via tag-based distant supervision. We achieve state-of-the-art performance on the second dataset, but not on the one labelled manually, indicating a difference between intended sarcasm, captured by distant supervision, and perceived sarcasm, captured by manual labelling.

AB - We investigate the impact of using author context on textual sarcasm detection. We deﬁne author context as the embedded representation of their historical posts on Twitter and suggest neural models that extract these representations. We experiment with two tweet datasets, one labelled manually for sarcasm, and the other via tag-based distant supervision. We achieve state-of-the-art performance on the second dataset, but not on the one labelled manually, indicating a difference between intended sarcasm, captured by distant supervision, and perceived sarcasm, captured by manual labelling.

M3 - Conference contribution

SP - 2854

EP - 2859

BT - Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

A2 - Korhonen, Anna

A2 - Traum, David

A2 - Màrquez, Lluís

PB - Association for Computational Linguistics (ACL)

CY - Florence, Italy

T2 - 57th Annual Meeting of the Association for Computational Linguistics

Y2 - 28 July 2019 through 2 August 2019

ER -

Exploring Author Context for Detecting Intended vs Perceived Sarcasm

Abstract / Description of output

Conference

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