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Sarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face.
Emotion recognition in conversation: Research challenges, datasets, and recent advances
Soujanya Poria, Navonil Majumder, Rada Mihalcea, and Eduard Hovy. 2019 · 1905
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Tensor fusion network for multimodal sentiment analysis
Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. 2017 · 1906
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Relations between two sets of variates
Harold Hotelling. 1936 · 1936
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Lower, slower, louder: Vocal cues of sarcasm
Patricia Rockwell. 2000 · 2000
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Multimodal markers of irony and sarcasm
Salvatore Attardo, Jodi Eisterhold, Jennifer Hay, and Isabella Poggi. 2003 · 2003
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Comparison of support vector machine and artificial neural network systems for drug/nondrug classification
Evgeny Byvatov, Uli Fechner, Jens Sadowski, and Gisbert Schneider. 2003 · 2003
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" yeah right": Sarcasm recognition for spoken dialogue systems
Joseph Tepperman, David Traum, and Shrikanth Narayanan. 2006 · 2006
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The sound of sarcasm
Henry S Cheang and Marc D Pell. 2008 · 2008
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Clues for detecting irony in user-generated contents: oh…!! it’s so easy;-
Paula Carvalho, Luís Sarmento, Mário J Silva, and Eugénio De Oliveira. 2009 · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
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Prosodic contrasts in ironic speech
Gregory A Bryant. 2010 · 2010
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Semi-supervised recognition of sarcastic sentences in twitter and amazon
Dmitry Davidov, Oren Tsur, and Ari Rappoport. 2010 · 2010
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Detecting ironic intent in creative comparisons
Tony Veale and Yanfen Hao. 2010 · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
Cited alongside, same era.
Context and intonation in the perception of sarcasm
Jennifer Woodland and Daniel Voyer. 2011 · 2011
Cited alongside, same era.
The perfect solution for detecting sarcasm in tweets# not
CC Liebrecht, FA Kunneman, and APJ van Den Bosch. 2013 · 2013
Cited alongside, same era.
Sarcasm as contrast between a positive sentiment and negative situation
Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalindra De Silva, Nathan Gilbert, and Ruihong Huang. 2013 · 2013
Cited alongside, same era.
Testing theories of irony processing using eye-tracking and erps
Ruth Filik, Hartmut Leuthold, Katie Wallington, and Jemma Page. 2014 · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
A deeper look into sarcastic tweets using deep convolutional neural networks
Soujanya Poria, Erik Cambria, Devamanyu Hazarika, and Prateek Vij. 2016 · 2016
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Detecting sarcasm in multimodal social platforms
R Schifanella, P de Juan, J Tetreault, L Cao, et al. 2016 · 2016
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Emotional responses to irony and emoticons in written language: evidence from eda and facial emg
Dominic Thompson, Ian G Mackenzie, Hartmut Leuthold, and Ruth Filik. 2016 · 2016
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Modelling context with user embeddings for sarcasm detection in social media
Silvio Amir Byron C Wallace, Hao Lyu, and Paula Carvalho Mário J Silva. 2016 · 2016
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Learning cognitive features from gaze data for sentiment and sarcasm classification using convolutional neural network
Abhijit Mishra, Kuntal Dey, and Pushpak Bhattacharyya. 2017 · 2017
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Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Cited alongside, same era.
Humans require context to infer ironic intent (so computers probably do, too)
Byron C Wallace, Laura Kertz, Eugene Charniak, et al. 2014 · 2014
Cited alongside, same era.
Contextualized sarcasm detection on twitter
David Bamman and Noah A Smith. 2015 · 2015
Cited alongside, same era.
Harnessing context incongruity for sarcasm detection
Aditya Joshi, Vinita Sharma, and Pushpak Bhattacharyya. 2015 · 2015
Cited alongside, same era.
Sarcasm detection on twitter: A behavioral modeling approach
Ashwin Rajadesingan, Reza Zafarani, and Huan Liu. 2015 · 2015
Cited alongside, same era.
Sparse, contextually informed models for irony detection: Exploiting user communities, entities and sentiment
Byron C Wallace, Eugene Charniak, et al. 2015 · 2015
Cited alongside, same era.
Putting sarcasm detection into context: The effects of class imbalance and manual labelling on supervised machine classification of twitter conversations
Gavin Abercrombie and Dirk Hovy. 2016 · 2016
Cited alongside, same era.
Zhe Cao, Gines Hidalgo, Tomas Simon, Shih-En Wei, and Yaser Sheikh. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Cascade: Contextual sarcasm detection in online discussion forums
Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, Erik Cambria, Roger Zimmermann, and Rada Mihalcea. 2018 · 2018
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Representing social media users for sarcasm detection
Y Alex Kolchinski and Christopher Potts. 2018 · 2018
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Dialoguernn: An attentive rnn for emotion detection in conversations
Navonil Majumder, Soujanya Poria, Devamanyu Hazarika, Rada Mihalcea, Alexander Gelbukh, and Erik Cambria. 2018 · 2018
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librosa/librosa: 0.6.2
Brian McFee, Matt McVicar, Stefan Balke, Carl Thomé, Vincent Lostanlen, Colin Raffel, Dana Lee, Oriol Nieto, Eric Battenberg, Dan Ellis, Ryuichi Yamamoto, Josh Moore, WZY, Rachel Bittner, Keunwoo Choi, Pius Friesch, Fabian-Robert Stöter, Matt Vollrath, Siddhartha Kumar, nehz, Simon Waloschek, Seth, Rimvydas Naktinis, Douglas Repetto, Curtis "Fjord" Hawthorne, CJ Carr, João Felipe Santos, JackieWu, Erik, and Adrian Holovaty. 2018 · 2018
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Meld: A multimodal multi-party dataset for emotion recognition in conversations
Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, and Rada Mihalcea. 2018 · 2018
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Capturing, representing, and interacting with laughter
Kimiko Ryokai, Elena Durán López, Noura Howell, Jon Gillick, and David Bamman. 2018 · 2018
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