International Journal of Engineering Research in Electronics and Communication Engineering

Implementation of the Behavioral Modeling Approach for Sarcasm Detection

Author : Geeta Mehetre ¹ M. B. Kalkumbe ²

Date of Publication :13th February 2018

Abstract: Sarcasm is a function of "sarcastic" or "non-sarcastic" labeling. It is a challenging task because there is no pronunciation or sarcasm. Facial expressions in the text However, humans can still see the feeling of severity in the text and the reasons for it. The perception of the friction of the text is an important task for the processing of natural language to avoid the erroneous interpretation of the text in the form of text. The accuracy and durability of the NLP model are often affected by a sense of dishonesty, which is often a mockery. Therefore, it is important to filter the vocal data of training information for various tasks related to NLP. "I'm excited to be called to work all weekend!" It can be classified as a highly positive feeling. However, the fact that negative feeling is implied intelligently through cynicism. The use of cynicism prevails in social networks, sub blogs and forms of electronic commerce. Cramp inspections are necessary for the correct confidence analysis and mining reviews. It can help improve automatic response in the context of client-based sites. Twitter is a small-scale blog platform widely used by people to comment, debate, discuss current events and convey information. Short Message Context the relevant context of the tweets is often identified using the Twitter # (hash-tag) data. It is a rich data repository for implicit sentences that have cynicism.

Reference :

1. Carvalho, P., Sarmento, S., Silva, M. J., and de Oliveira, E. 2009. Clues for detecting irony in user-generated contents: oh...!! it's "so easy" ;-). In Proceeding of the 1st international CIKM workshop on Topicsentiment analysis for mass opinion (TSA '09). ACM, New York, NY, USA, 53-56.
2. Clark, H. and Gerrig, R. 1984. On the pretence theory of irony. Journal of Experimental Psychology: General, 113:121– 126. D.C.
3. Davidov, D., Tsur, O., and Rappoport, A. 2010. SemiSupervised Recognition of Sarcastic Sentences in Twitter and Amazon, Dmitry Proceeding of Computational Natural Language Learning (ACL-CoNLL).
4. Derks, D., Bos, A. E. R., and Grumbkow, J. V. 2008. Emoticons and Online Message Interpretation. Soc. Sci. Comput. Rev., 26(3), 379-388.
5. Gibbs, R. 1986. On the psycholinguistics of sarcasm. Journal of Experimental Psychology: General, 105:3–15.
6. Gibbs, R. W. and Colston H. L. eds. 2007. Irony in Language and Thought. Routledge (Taylor and Francis), New York.
7. Kreuz, R. J. and Glucksberg, S. 1989. How to be sarcastic: The echoic reminder theory of verbal irony. Journal of Experimental Psychology: General, 118:374-386.