Standard
A Survey of Multimodal Sarcasm Detection. / Farabi, Shafkat
; Ranasinghe, Tharindu; Kanojia, Diptesh et al.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. ed. / Kate Larson. Jeju: International Joint Conferences on Artificial Intelligence Organization, 2024. p. 8020-8028.
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Farabi, S
, Ranasinghe, T, Kanojia, D, Kong, Y & Zampieri, M 2024,
A Survey of Multimodal Sarcasm Detection. in K Larson (ed.),
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, Jeju, pp. 8020-8028, The 33rd International Joint Conference on Artificial Intelligence, Jeju, Korea, Republic of,
3/08/24.
https://doi.org/10.24963/ijcai.2024/887APA
Farabi, S.
, Ranasinghe, T., Kanojia, D., Kong, Y., & Zampieri, M. (2024).
A Survey of Multimodal Sarcasm Detection. In K. Larson (Ed.),
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (pp. 8020-8028). International Joint Conferences on Artificial Intelligence Organization.
https://doi.org/10.24963/ijcai.2024/887Vancouver
Farabi S
, Ranasinghe T, Kanojia D, Kong Y, Zampieri M.
A Survey of Multimodal Sarcasm Detection. In Larson K, editor, Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. Jeju: International Joint Conferences on Artificial Intelligence Organization. 2024. p. 8020-8028 doi: 10.24963/ijcai.2024/887
Author
Farabi, Shafkat
; Ranasinghe, Tharindu ; Kanojia, Diptesh et al. /
A Survey of Multimodal Sarcasm Detection. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. editor / Kate Larson. Jeju : International Joint Conferences on Artificial Intelligence Organization, 2024. pp. 8020-8028
Bibtex
@inproceedings{559ca71c49c34dec9f65ba077ce49027,
title = "A Survey of Multimodal Sarcasm Detection",
abstract = "Sarcasm is a rhetorical device that is used to convey the opposite of the literal meaning of an utterance. Sarcasm is widely used on social media and other forms of computer-mediated communication motivating the use of computational models to identify it automatically. While the clear majority of approaches to sarcasm detection have been carried out on text only, sarcasm detection often requires additional information present in tonality, facial expression, and contextual images. This has led to the introduction of multimodal models, opening the possibility to detect sarcasm in multiple modalities such as audio, images, text, and video. In this paper, we present the first comprehensive survey on multimodal sarcasm detection - henceforth MSD - to date. We survey papers published between 2018 and 2023 on the topic, and discuss the models and datasets used for this task. We also present future research directions in MSD.",
author = "Shafkat Farabi and Tharindu Ranasinghe and Diptesh Kanojia and Yu Kong and Marcos Zampieri",
year = "2024",
month = aug,
day = "6",
doi = "10.24963/ijcai.2024/887",
language = "English",
pages = "8020--8028",
editor = "Kate Larson",
booktitle = "Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence Organization",
note = "The 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 ; Conference date: 03-08-2024 Through 09-08-2024",
}
RIS
TY - GEN
T1 - A Survey of Multimodal Sarcasm Detection
AU - Farabi, Shafkat
AU - Ranasinghe, Tharindu
AU - Kanojia, Diptesh
AU - Kong, Yu
AU - Zampieri, Marcos
PY - 2024/8/6
Y1 - 2024/8/6
N2 - Sarcasm is a rhetorical device that is used to convey the opposite of the literal meaning of an utterance. Sarcasm is widely used on social media and other forms of computer-mediated communication motivating the use of computational models to identify it automatically. While the clear majority of approaches to sarcasm detection have been carried out on text only, sarcasm detection often requires additional information present in tonality, facial expression, and contextual images. This has led to the introduction of multimodal models, opening the possibility to detect sarcasm in multiple modalities such as audio, images, text, and video. In this paper, we present the first comprehensive survey on multimodal sarcasm detection - henceforth MSD - to date. We survey papers published between 2018 and 2023 on the topic, and discuss the models and datasets used for this task. We also present future research directions in MSD.
AB - Sarcasm is a rhetorical device that is used to convey the opposite of the literal meaning of an utterance. Sarcasm is widely used on social media and other forms of computer-mediated communication motivating the use of computational models to identify it automatically. While the clear majority of approaches to sarcasm detection have been carried out on text only, sarcasm detection often requires additional information present in tonality, facial expression, and contextual images. This has led to the introduction of multimodal models, opening the possibility to detect sarcasm in multiple modalities such as audio, images, text, and video. In this paper, we present the first comprehensive survey on multimodal sarcasm detection - henceforth MSD - to date. We survey papers published between 2018 and 2023 on the topic, and discuss the models and datasets used for this task. We also present future research directions in MSD.
U2 - 10.24963/ijcai.2024/887
DO - 10.24963/ijcai.2024/887
M3 - Conference contribution/Paper
SP - 8020
EP - 8028
BT - Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
A2 - Larson, Kate
PB - International Joint Conferences on Artificial Intelligence Organization
CY - Jeju
T2 - The 33rd International Joint Conference on Artificial Intelligence
Y2 - 3 August 2024 through 9 August 2024
ER -
