References
- Amir, S., Wallace, B.C., Lyu, H., & Silva, P.C.M.J. (2016). Modelling context with user embeddings for sarcasm detection in social media. arXiv preprint arXiv:1607.00976.
- Bamman, D., & Smith, N.A. (2015). Contextualized sarcasm detection on twitter. In Proceedings of the Ninth International AAAI Conference on Web and Social Media.
- Barbieri, F., Saggion, H., & Ronzano, F. (2014). Modelling sarcasm in twitter, a novel approach. In Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 50–58.
- Bharti, S.K., Babu, K.S., & Jena, S.K. (2015). Parsing-based sarcasm sentiment recognition in twitter data. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, 1373–1380. ACM.
- Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5, 135–146.
- Carvalho, P., Sarmento, L., Silva, M.J., & De Oliveira, E. (2009). Clues for detecting irony in user-generated contents: Oh...!! It’s “so easy”-. In Proceedings of the 1st International CIKM Workshop on Topic-sentiment Analysis for Mass Opinion, 53–56. ACM.
- Cheang, H.S., & Pell, M.D. (2008). The sound of sarcasm. Speech Communication, 50(5), 366– 381.
- Clark, H.H., & Gerrig, R.J. (1984). On the pretense theory of irony. American Psychological Association, 113(1), 121–126.
- Davidov, D., Tsur, O., & Rappoport, A. (2010). Semi-supervised recognition of sarcastic sentences in twitter and amazon. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning, 107–116. Association for Computational Linguistics.
- Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., & Lehmann, S. (2017). Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. arXiv preprint arXiv:1708.00524.
- Ghosh, D., & Muresan, S. (2018). “with 1 follower I must be AWESOME : P.”. Exploring the role of irony markers in irony recognition. In Proceedings of the Twelfth International Conference on Web and Social Media, ICWSM 2018, Stanford, California, USA, June 25–28, 2018.
- Gonzalez-Ibanez, R., Muresan, S., & Wacholder, N. (2011). Identifying sarcasm in twitter: A closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers, 2, 581–586. Association for Computational Linguistics.
- Hazarika, D., Poria, S., Gorantla, S., Cambria, E., Zimmermann, R., & Mihalcea, R. (2018). CASCADE: Contextual sarcasm detection in online discussion forums. CoRR, abs/1805.06413.
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
- Ivanko, S.L., & Pexman, P.M. (2003). Context incongruity and irony processing. Discourse Processes, 35(3), 241–279.
- Jorgensen, J., Miller, G.A., & Sperber, D. (1984). Test of the mention theory of irony. Journal of Experimental Psychology: General, 113(1), 112.
- Joshi, A., Sharma, V., & Bhattacharyya, P. (2015). Harnessing context incongruity for sarcasm detection. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 2, 757–762.
- Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.
- Kingma, D.P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
- Kolchinski, Y.A., & Potts, C. (2018). Representing social media users for sarcasm detection. arXiv preprint arXiv:1808.08470.
- LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P., et al. (1998). Gradient-based learning applied to document recognition. In Proceedings of the IEEE, 86(11), 2278–2324.
- Liebrecht, C., Kunneman, F., & van Den Bosch, A. (2013). The perfect solution for detecting sarcasm in tweets #not. Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA).
- Maynard, D., & Greenwood, M.A. (2014). Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. In Proceedings of LREC 2014. ELRA.
- Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. Workshop on ICLR.
- Mishra, R. (2018). Github news headlines dataset for sarcasm detection: High quality dataset for the task of sarcasm detection. Retrieved from https://www.kaggle.com/rmisra/news-headlines-dataset-for-sarcasm-detection
- Oraby, S., Harrison, V., Reed, L., Hernandez, E., Riloff, E., & Walker, M. (2017). Creating and characterizing a diverse corpus of sarcasm in dialogue. arXiv preprint arXiv:1709.05404.
- Pennington, J., Socher, R., & Manning, C.D. (2014). Glove: Global vectors for word representation. In Proceedings of EMNLP, 1532–1543. ACL.
- Poria, S., Cambria, E., Hazarika, D., & Vij, P. (2016). A deeper look into sarcastic tweets using deep convolutional neural networks. arXiv preprint arXiv:1610.08815.
- Rajadesingan, A., Zafarani, R., & Liu, H. (2015). Sarcasm detection on twitter: A behavioral modeling approach. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, 97–106. ACM.
- Riloff, E., Qadir, A., Surve, P., De Silva, L., Gilbert, N., & Huang, R. (2013). Sarcasm as contrast between a positive sentiment and negative situation. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 704–714.
- Rockwell, P. (2000). Lower, slower, louder: Vocal cues of sarcasm. Journal of Psycholinguistic Research, 29(5), 483–495.
- Tepperman, J., Traum, D., & Narayanan, S. (2006). “yeah right”: Sarcasm recognition for spoken dialogue systems. In Proceedings of the Ninth International Conference on Spoken Language.
- Tsur, O., Davidov, D., & Rappoport, A. (2010). A great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media.
- Wallace, B.C., Choe, D.K., & Charniak, E. (2015). Sparse, contextually informed models for irony detection: Exploiting user communities, entities and sentiment. In ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference (pp. 1035–1044). (ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference; Vol. 1). Association for Computational Linguistics (ACL).
- Wallace, B.C., Choe, D.K., Kertz, L., & Charniak, E. (2014). Humans require context to infer ironic intent (so computers probably do, too). In Long Papers (pp. 512–516). (52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference; Vol. 2). Association for Computational Linguistics (ACL).
- Wang, S.-H., Muhammad, K., Hong, J., Sangaiah, A.K., & Zhang, Y.-D. (2018). Alcoholism identification via convolutional neural network based on parametric relu, dropout, and batch normalization. Neural Computing and Applications, 1–16.
- Zhang, M., Zhang, Y., & Fu, G. (2016). Tweet sarcasm detection using deep neural network. In Proceedings of COLING 2016, The 26th International Conference on Computational Linguistics: Technical Papers, 2449–2460.