TY - GEN
T1 - Exploiting emojis for sarcasm detection
AU - Subramanian, Jayashree
AU - Sridharan, Varun
AU - Shu, Kai
AU - Liu, Huan
N1 - Funding Information:
Acknowledgements. This material is based upon work supported by, or in part by, the ONR grant N00014-17-1-2605 and N000141812108.
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Modern social media platforms largely rely on text. However, the written text lacks the emotional cues of spoken and face-to-face dialogue, ambiguities are common, which is exacerbated in the short, informal nature of many social media posts. Sarcasm represents the nuanced form of language that individuals state the opposite of what is implied. Sarcasm detection on social media is important for users to understand the underlying messages. The majority of existing sarcasm detection algorithms focus on text information; while emotion information expressed such as emojis are ignored. In real scenarios, emojis are widely used as emotion signals, which have great potentials to advance sarcasm detection. Therefore, in this paper, we study the novel problem of exploiting emojis for sarcasm detection on social media. We propose a new framework ESD, which simultaneously captures various signals from text and emojis for sarcasm detection. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework.
AB - Modern social media platforms largely rely on text. However, the written text lacks the emotional cues of spoken and face-to-face dialogue, ambiguities are common, which is exacerbated in the short, informal nature of many social media posts. Sarcasm represents the nuanced form of language that individuals state the opposite of what is implied. Sarcasm detection on social media is important for users to understand the underlying messages. The majority of existing sarcasm detection algorithms focus on text information; while emotion information expressed such as emojis are ignored. In real scenarios, emojis are widely used as emotion signals, which have great potentials to advance sarcasm detection. Therefore, in this paper, we study the novel problem of exploiting emojis for sarcasm detection on social media. We propose a new framework ESD, which simultaneously captures various signals from text and emojis for sarcasm detection. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework.
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U2 - 10.1007/978-3-030-21741-9_8
DO - 10.1007/978-3-030-21741-9_8
M3 - Conference contribution
AN - SCOPUS:85068115723
SN - 9783030217402
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 70
EP - 80
BT - Social, Cultural, and Behavioral Modeling - 12th International Conference, SBP-BRiMS 2019, Proceedings
A2 - Thomson, Robert
A2 - Bisgin, Halil
A2 - Dancy, Christopher
A2 - Hyder, Ayaz
PB - Springer Verlag
T2 - 12th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2019
Y2 - 9 July 2019 through 12 July 2019
ER -