The week's pick
Reseach Article
Optimal Feature Extraction based Machine Learning Approach for Sarcasm Type Detection in News Headlines
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 177 - Number 46 |
| Year of Publication: 2020 |
| Authors: Vaishvi Prayag Jariwala |
10.5120/ijca2020919981
|
Vaishvi Prayag Jariwala . Optimal Feature Extraction based Machine Learning Approach for Sarcasm Type Detection in News Headlines. International Journal of Computer Applications. 177, 46 ( Mar 2020), 25-29. DOI=10.5120/ijca2020919981
Abstract
Sarcasm detection has received increasing research in recent years. Detection of sarcasm is of great importance and beneficial to many NLP applications, such as sentiment analysis, opinion mining and advertising. Detection of sarcasm is of great importance and beneficial to many NLP applications, such as sentiment analysis, opinion mining and advertising. Generally, sarcasm detection task is treated as standard test classification problem. Sarcasm is the unconventional way of conveying a message which conflicts the context. It can lead to a state of ambiguity. As Sarcasm represents contrary sentiment to the literal meaning that is conveyed in the text, it is hard to identify sarcasm even for a human. Existing models mainly focus on designing effective features for improving the detection performance. In this paper, optimal features are to be selected before data passes to classification task. So data pre-processing makes the data clean so that the performance of the classifier will be enhance. Result shows the improve performance in sarcasm detection using the optimal feature sets.
References
- Mondher Bouazizi and Tomoaki Otsuki, “A Pattern-Based Approach for Sarcasm Detection on Twitter,” IEEE Access Volume 4, 2016. pp. 5477- 5488.
- Shuigui Huang, Wenwen Han, Xirong Que and Wendong Wang, “Polarity Identification of Sentiment Words based on Emoticons,” International Conference on Computational Intelligence and Security, 2017. Pp 134–138.
- S. Homoceanu, M. Loster, C. Lo_, and W.-T. Balke, ``Will I like it? Providing product overviews based on opinion excerpts,'' in Proc. IEEE CEC, Sep. 2011, pp. 26_33.
- Deirdre Wilson. 2006. The pragmatics of verbal irony: Echo or pretence? Lingua 116(10):1722–1743.
- Rachel Giora. 1995. On irony and negation. Discourse processes 19(2):239–264.
- Riloff, Ellen, et al. "Sarcasm as contrast between a positive sentiment and negative situation." Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 2013.
- Utsumi,A.(2004).Stylistic and contextual effects in irony processing. In Proceedings of the 26th Annual Meeting of the Cognitive Science Society, pages 1369–1374.
- Sana Parveen, Sachin N. Deshmukh, “Opinion Mining in Twitter – Sarcasm Detection” International Research Journal of Engineering and Technology (IRJET), volume 04, issue 10, pages 201-204, October 2017.
- Manoj Y. Manohar, PallaviKulkarni, “Improvement Sarcasm Analysis using NLP and Corpus based Approach”, International Conference on Intelligence Computing and Control Systems (ICICCS), IEEE, 2017.
- Reyes, A., Rosso, P., and Buscaldi, D.(2012). From humor recognition to irony detection: The figurative language of social media. Data & Knowledge Engineering, 74:1–12.
- Richard Socher, Alex Perelygin, Jean Y Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the EMNLP.
- Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. 2014. A convolutional neural network for modeling sentences. In Proceedings of the 52nd ACL, pages 655–665. Association for Computational Linguistics.
- Shusen Zhou, Qingcai Chen, Xiaolong Wang, and Xiaoling Li. 2014. Hybrid deep belief networks for semisupervised sentiment classification. In Proceedings of COLING 2014, pages 1341–1349. Dublin City University and Association for Computational Linguistics.
- Cicero dos Santos and Maira Gatti. 2014. Deep convolutional neural networks for sentiment analysis of short texts. In Proceedings of COLING 2014, pages 69–78.
- Duy-Tin Vo and Yue Zhang. 2015. Target-dependent twitter sentiment classification with rich automatic features. In Proceedings of IJCAI, BueNos Aires, Argentina, August.
- Silvio Amir, Byron C. Wallace, Hao Lyu, Paula Carvalho, and Mario J. Silva. 2016. Modelling context with user embeddings for sarcasm detection in social media. In CONLL 2016.
- Aniruddha Ghosh and Tony Veale. 2016. Fracking sarcasm using neural network. In Proceedings of the 7th WASSA, pages 161–169.
- Tom´as Pt´acek, Ivan Habernal, and Jun Hong. 2014. Sarcasm detection on Czech and English Twitter. In COLING, pages 213–223.
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