An Effective Approach for Sarcasm Detection in Text Data for Sentimental Analysis

  • Authors

    • Adarsh M J
    • Dr. Pushpa Ravikumar
    2018-12-13
    https://doi.org/10.14419/ijet.v7i4.39.23825
  • Polarity, Sarcasm, Score, Sentiment

  • The stream of Sentiment Analysis has become very popular today helping people and corporate to analyze the orientation of sentiments towards particular product and people. The Sentiment Analysis will not be complete without analyzing the Sarcasm or Irony in statements. Sarcasm is the art of saying something opposite to the original meaning in a sentence. Most of the times Sarcasm in sentences makes it more Negative than positive. In this paper, an approach is adopted to identify the sarcasm in sentences using Sentiwordnet. A set of core popular sarcastic sentences are considered and scores are calculated. The scores points out at the sarcasm in sentences which most of the time is negative. The polarities of sentences are also calculated and also the sentences are checked for sarcasm scores in Sarcasm detector tool.

     

     

  • References

    1. [1] Ellen riloff, Ashequl Qadir, Prafulla Surve, Lalindra De Silva, Nathan Gilbert, Ruihong Hunag, “ Sarcasm as Contrast between a Positive sentiment and Negative Situationâ€, Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP, 18-21 October 2013, Seattle, USA.

      [2] Mondher Bouazizi, Tomoaki ohtsuki, “ Sarcasm Detection in Twitter-All your products are incredibly amazing, are they really ?â€, IEEE Global Communications Conference (GLOBECOM), 6-10 Dec. 2015, San Diego, CA, USA.

      [3] Mondher Bouazizi, Tomoaki ohtsuki, “ Opinion Mining in twitter-How to make use of sarcasm to enhance sentiment analysisâ€, IEEE/ACM International conference on Advances in Social Networks and Mining(ASONAM), 25-28 August 2015, Paris, France.

      [4] Santosh Kumar Bharti, Korra Sathya Babu, Sanjay Kumar Jena, “Parsing based sarcasm sentiment recognition in Twitter Dataâ€, IEEE/ACM International conference on Advances in Social Networks and Mining(ASONAM) ), 25-28 August 2015, Paris, France.

      [5] S K Bharti, B Vachha, R K Pradhan, K S Babu, S K Jena, “ Sarcastic sentiment detection in tweets streamed in real time: A BigData approachâ€, Elsevier, 2016.

      [6] Ashwin Rajadesingan, “Detecting Sarcasm on Twitter: A Behaviour Modeling approachâ€, Arizona state University, 2014.

      [7] Aditya Joshi, pushpak Bhattacharyya, Mark j Carman, “Automatic Sarcasm detection: A surveyâ€, ACM computing Surveys, 2017.

      [8] http://www.thesarcasmdetector.com/

      [9] http://sentiwordnet.isti.cnr.it/

      [10] https://textblob.readthedocs.io/en/dev/

  • Downloads

  • How to Cite

    M J, A., & Pushpa Ravikumar, D. (2018). An Effective Approach for Sarcasm Detection in Text Data for Sentimental Analysis. International Journal of Engineering & Technology, 7(4.39), 136-138. https://doi.org/10.14419/ijet.v7i4.39.23825