Shuaiqi He
ABSTRACT
Sarcasm is a form of figurative language where the literal meaning of words does not hold, and instead, the opposite interpretation is intended. This deliberate ambiguity makes sarcasm detection an important task in sentiment analysis. Sarcasm detection is considered to be a binary classification problem. Traditional works rely heavily on feature-rich traditional models and deep learning models. However, in the model training, previous works worked with a sentence or comment as a unit, they may lose some important global features and ignore rich relational structures in the corpus. In contrast, if we use the whole sarcasm corpus to construct a graph network in the model, we can learn to get representations with global information under sarcasm background. In this work, we propose a new type of neural network model. Specifically, we use a graph convolutional neural(GCN) network to capture the features of global information in the satire context and jointly bidirectional LSTM(bi-LSTM) neural network to capture the sequence features of the comments respectively. And finally, we concatenate these two embeddings and put them into a traditional classifier for classification prediction. Experiment results include precision, accuracy, recall and F1 score on the publicly available dataset show that our model is significantly better than the standard evaluation model benchmarks.
- Bo Pang, Lillian Lee, et al. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2): 1--135Google Scholar
Digital Library
- SilvioAmir, ByronCWallace, Hao Lyu, and Paula Carvalho Mário J Silva. 2016. Modeling context with user embeddings for sarcasm detection in social media. arXiv preprint arXiv:1607.00976 (2016).Google Scholar
- Aniruddha Ghosh and Tony Veale. 2016. Fracking sarcasm using neural network. In Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and social media analysis. 161--169.Google ScholarCross Ref
- Yi Tay, Luu Anh Tuan, Siu Cheung Hui, and Jian Su. 2018. Reasoning with Sarcasm by Reading In-between. arXiv preprint arXiv:1805.02856 (2018).Google Scholar
- Meishan Zhang, Yue Zhang, and Guohong Fu. 2016. Tweet sarcasm detection using deep neural network. In Proceedings of COLING 2016, The 26th International Conference on Computational Linguistics: Technical Papers. 2449--2460.Google Scholar
- Battaglia, P.W.; Hamrick, J.B.; Bapst, V.; Sanchez-Gonzalez, A.; Zambaldi, V.; Malinowski, M.; Tacchetti, A.; Raposo, D.; Santoro, A.; Faulkner, R.; et al. 2018. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261.Google Scholar
- Peng, H.; Li, J.; He, Y.; Liu, Y.; Bao, M.; Wang, L.; Song, Y.; and Yang, Q. 2018. Large-scale hierarchical text classification with recursively regularized deep graph-cnn. In WWW, 1063--1072.Google Scholar
- Cai, H.; Zheng, V. W.; and Chang, K. 2018. A comprehensive survey of graph embedding: problems, techniques and applications. IEEE Transactions on Knowledge and Data Engineering 30(9): 1616- 1637.Google ScholarDigital Library
- Diana Maynard and Mark A. Greenwood. 2014. Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. In Proceedings of the Conference on Language Resource and Evaluation (LREC'14).Google Scholar
- Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalindra De Silva, Nathan Gilbert, and Ruihong Huang. 2013. Sarcasm as contrast between a positive sentiment and negative situation. In Proceedings of the Conference on Empirical Methods for Natural Language Processing (EMNLP'13). 704--714.Google Scholar
- Roberto González-Ibánez, Smaranda Muresan, and Nina Wacholder. 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-Volume 2. Association for Computational Linguistics, 581--586.Google ScholarDigital Library
- Zelin Wang, Zhijian Wu, Ruimin Wang, and Yafeng Ren. 2015. Twitter sarcasm detection exploiting a context-based model. In Web Information Systems Engineering--WISE 2015. Springer, 77--91.Google ScholarDigital Library
- Basilis Charalampakis, Dimitris Spathis, Elias Kouslis, and Katia Kermanidis. 2016. A comparison between semi-supervised and supervised text mining techniques on detecting irony in greek political tweets. Engineering Applications of Artificial Intelligence (2016). DOI= http://dx.doi.org/10.1016/j.engappai.2016.01.007Google ScholarDigital Library
- Amir Silvio, Byron C. Wallace, Hao Lyu, and Paula Carvalho Mário J Silva. 2016. Modelling context with user embeddings for sarcasm detection in social media. Proceedings of CoNLL 2016. 167.Google Scholar
- Soujanya Poria, Erik Cambria, Devamanyu Hazarika, and Prateek Vij. 2016. A deeper look into sarcastic tweets using deep convolutional neural networks. arXiv:1610.08815 (2016).Google Scholar
- Kipf, T. N., and Welling, M. 2017. Semi-supervised classification with graph convolutional networks. In ICLR.Google Scholar
- Li, Q.; Han, Z.; and Wu, X. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In AAAI.Google Scholar
- Mikhail Khodak, Nikunj Saunshi, and Kiran Vodrahalli. 2017. A large self-annotated corpus for sarcasm. arXiv preprint arXiv:1704.05579.Google Scholar
- Meishan Zhang, Yue Zhang, and Guohong Fu. 2016. Tweet sarcasm detection using deep neural network. In COLING 2016. 26th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, December 11-16, 2016, Osaka, Japan. 2449--2460.Google Scholar
- Aniruddha Ghosh and Tony Veale. 2017. Magnets for sarcasm: Making sarcasm detection timely, contextual and very personal. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9-11, 2017. 482--491.Google ScholarCross Ref
Index Terms
- Sarcasm Detection Using Graph Convolutional Networks with Bidirectional LSTM
Recommendations
Using LSTM for Context Based Approach of Sarcasm Detection in Twitter
IAIT '20: Proceedings of the 11th International Conference on Advances in Information TechnologyIn this research, we propose a sarcasm detection by taking into consideration its many varying contexts, related to the word or phrase in a tweet. To get the related context, we extract the information with paragraph2vec to simplify the process of ...
Sarcasm Detection on Twitter: A Behavioral Modeling Approach
WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data MiningSarcasm is a nuanced form of language in which individuals state the opposite of what is implied. With this intentional ambiguity, sarcasm detection has always been a challenging task, even for humans. Current approaches to automatic sarcasm detection ...
Sarcasm Detection Using Bidirectional Encoder Representations from Transformers and Graph Convolutional Networks
AbstractThe Internet has become a crucial space for customer feedback and the budding of various ideologies across different cultures. But some people provide their opinions whose sole meaning is different from what their figurative meanings imply. This ...
Comments