International Journal of Science , Engineering and Management (IJSEM)

Monthly Journal for Science , Engineering and Management

Volume3,February 2018,

Topic : Implementation of the Behavioral Modeling Approach for Sarcasm Detection

Authors:Geeta Mehetre || M. B. Kalkumbe

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..

Keywords: Hashtags, Linguistics, Opinion Mining, Sarcasm Detection, Tweets, Stanford NLP.

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DOI: 01.1617/vol5/iss2/pid49285

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Dr.Muhammad Ishtiaq,
Mirpur University of
Science & Technology,
Mirpur, Azad Kasmir.

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ISSN(Online): 2456-1304

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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Journal Code : IJSEM
Electronic ISSN : 2456-1304
Impact Factor : 2.7
Frequency : Monthly
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