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An Approach to Detect Sarcasm in Tweets

EasyChair Preprint no. 3035

10 pagesDate: March 23, 2020


When academic and business ventures are discussed, electronic documents form the crucial part of receiving and transferring information. There is no use of online information if we cannot extract it and use it to cater our ventures. In order to frame up any summary, it is required to nd the relevant text with complete omission of unnecessary information while keeping the focus on details and compile them into a document. The sentiment analysis is the approach used to evaluate users' sentiments on websites, forums, comments, feedback as negative, positive or neutral. But, sometimes, people express their negative sentiment in a positive manner. This ips the polarity of the sentence and sentiment analysis performance is aected. Thus, detection of sarcasm is an important part of sentiment analysis. Input data features are extracted and data needs to be classied as sarcastic or not. To increase accuracy for the sarcasm detection from twitter data new features needs to add fort the training. This paper proposed to develop an ensemble classication method having base classiers as Decision Tree, Naive Bayes and K-nearest Neighbor to increase various parametric values for the sarcasm detection.

Keyphrases: Ensemble Classier, machine learning, Sarcasm, Sentiment Analysis

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Jyoti Godara and Rajni Aron},
  title = {An Approach to Detect Sarcasm in Tweets},
  howpublished = {EasyChair Preprint no. 3035},

  year = {EasyChair, 2020}}
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