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Proposed Approach for Sarcasm Detection in Twitter


  • Department of Computer Science, Central University of South Bihar, Patna – 800014, Bihar, India


Background: Sarcasm detection in twitter is a very important task as it had helped in the analysis of tweets. With the help of sarcasm detection, companies could analyze the feelings of user about their products. This is helpful for companies, as the companies could improve their quality of product. Methods: For preprocessing of data TextBlob is used. TextBlob is a package installed in Natural Language Toolkit. The preprocessing steps include tokenization, part of speech tagging and parsing. The stop words are removed using python programming. The stop words corpus which consist of 2400 stop words and which is distributed with NLTK have been used. RapidMiner is used for finding polarity and subjectivity of tweets. TextBlob is used for finding the polarity and subjectivity confidence. Weka is used for calculating the accuracy of tweets based on Naïve Bayes classifier and SVM classifiers. Findings: The paper provides the polarity of tweets which include whether the tweet is positive, negative or neutral. Polarity confidence and subjectivity confidence are also found. Accuracy of tweets are found using Naïve Bayes and SVM classifiers. Applications: Sarcasm Detection could be helpful in analyzing the exact opinion of the user about a certain thing.


Polarity, Polarity Confidence, Sarcasm Detection, Subjectivity, Subjectivity Confidence.

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  • Cambell JD, Katz AN. Are there necessary conditions for inducing a sense of sarcastic irony, Discourse Processes. 2012; 49 (6):459-80. Crossref.
  • Liebrecht C, Kunneman F, Bosch AVD. The perfect solution for detecting sarcasm in tweets# not. Radbound University. 2013; 1-9.
  • Maynard D, Greenwood MA. Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis. 2014; 1-6. PMid:25320772.
  • Rajadesingan A, Zafarani R, Liu H. Sarcasm detection on twitter: A behavioral modeling approach, Web Search and Data Mining. 2015; 97-106. Crossref.
  • Ravichandran SP. Sarcasm Detection in Twitter data, Rochester Institute of Technology, 2015; 1-26. PMid:25450559 .
  • Kreuz RJ, Glucksberg S. How to be sarcastic: The echoic reminder theory of verbal irony, Journal of Experimental Psychology. 1989; 118(4):374-86. Crossref.
  • Carvalho P, Sarmento L, Silva MJ. Clues for detecting irony in user-generated contents: oh!! It’s so easy. Proceedings of the 1st. 2009; 53-56. Crossref.
  • Lunando E, Purwarianti A. Indonesian social media sentiment analysis with sarcasm detection, Advanced Computer Science. 2013; 195-98. Crossref.
  • Joshi A, Bhattacharyya P, Carman MJ. Automatic sarcasm detection: A survey. arXiv preprint arXiv:1602.03426. 2016.
  • Bharti SK, Vachha B, Pradhan RK, Babu KS. Sarcastic sentiment detection in tweets streamed in real time: A big data approach, Digital Communications. 2016; 1-17. PMCid:PMC4741291.
  • Pradhan VM, Vala J, Balani P. A Survey on sentiment analysis algorithms for opinion mining, International Journal of Computer. 2016; 133(9):1-5.
  • Tsur O, Davidov D, Aappoport A. ICWSM-A great catchy name: semi-supervised recognition of sarcastic sentences in online product reviews.2010; 1-8.
  • Tayal DK, Yadav S, Gupta K, Rajput B. Polarity detection of sarcastic political tweets, Sustainable Global. 2014. Crossref.
  • Utsumi A.Verbal irony as implicit display of ironic environment: Distinguishing ironic utterances from nonirony, Journal of Pragmatics. 2000; 32(12):1777-806. Crossref.
  • Filatova E. Irony and sarcasm: Corpus generation and analysis using crowd sourcing, LREC. 2012; 1-7.
  • Tungthamthiti P, Shirai K, Mohd M. Recognition of sarcasms in tweets based on concept level sentiment analysis and supervised learning approaches, PACLIC. 2014; 1-10.
  • Giora R. On irony and negation. Discourse processes, Taylor and Francis. 1995; 19(2):239-64.
  • Lee CJ, Katz AN. The differential role of ridicule in sarcasm and irony, Taylor and Francis. 1998; 13(1):1-15.
  • Toplak M, Katz AN. On the uses of sarcastic irony, Journal of Pragmatics. 2000; 32(10):1467-88. Crossref.
  • Nakamura SK, Glucksberg S. How about another piece of pie: The allusional pretense theory of discourse irony, Journal of Experimental. 1995; 124(1):3-21.
  • Riloff E, Qadir A, Surve P, Silva DL, Gilbert N. Sarcasm as contrast between a positive sentiment and negative situation. 2013; 1-11.
  • Maynard D, Greenwood MA. Who cares about sarcastic tweets? Investigating the Impact of Sarcasm on Sentiment Analysis, LREC. 2014; 1-6. PMid:25320772.
  • Davidov D, Tsur O. Semi-supervised recognition of sarcastic sentences in twitter and amazon, ACM. 2010; 1-10.
  • Pak A, Paroubek P. Twitter as a corpus for sentiment analysis and opinion mining, LREC. 2010; 1-7.
  • Parikh R., Movassate M. Sentiment analysis of user-generated twitter updates using various classification techniques, CS224N Final Report. 2009; 1-18.
  • Bifet A, Frank E. Sentiment knowledge discovery in twitter streaming data, International Conference on Discovery Science. 2010; 1-15. Crossref.
  • Gamallo P, Garcia M. Citius: A naive-bayes strategy for sentiment analysis on English tweets, Proceedings of SemEval. 2014; 1-5.
  • Ibánez RG, Muresan S. Identifying sarcasm in Twitter: A closer look, ACM. 2011; 581-86.


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