A Novel Integrated Framework for Sarcasm Detection in Social Platform
Shawni Dutta1, Akash Mehta2, Samir Kumar Bandyopadhyay3
1Shawni Dutta, Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India.
2Akash Mehta, Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India.
3Samir Kumar Bandyopadhyay*, Academic Advisor, The Bhawanipur Education Society College, Kolkata, India.
Manuscript received on March 29, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 943-950 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7519049420/2020©BEIESP | DOI: 10.35940/ijeat.D7519.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sarcasm is a form of speech which transforms the verbatim meaning of a sentence into its antonym. Sarcasm identification in social media is a crucial facet of the sentiment analysis process, since it deals with texts whose polarity is completely opposite from its utterance. Our paper provides an exhaustive review of the existing methodologies dedicated to the task of detecting sarcasm in texts posted on an online forum. A comparative analysis of the existing techniques, mentioning the datasets and the performance measure, is also provided. This paper also introduces a novel integrated framework for identifying sarcastic clues in tweets, and recognizing sarcastic users.
Keywords: Sarcasm Detection, Lexical Analysis, Natural Language Processing, Big Data, Deep Learning, MSELA.