Sarcasm Detection using Naïve Bayes SVM Hybrid
Ashwini M Joshi1, Sameer Prabhune2, Divya Jyoti B N3

1Ashwini Joshi, Department of CSE, SGBAU, Amaravati, Maharashtra, India.
2Sameer Prabhune, Department of CSE, SGBAU, Amaravati, Maharashtra, India.

3Divya Jyoti B N, Department of CSE, PES University, Bngalore, Karnataka, India.

Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 1138-1142 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4258098319/19©BEIESP | DOI: 10.35940/ijrte.C4258.098319
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Abstract: Sarcasm detection plays a vital role in Sentiment analysis and sentiment classification as the occurrence of sarcasm in an input text may drive Sentiment Analysis job in different (Wrong) classification. Our research work aims in sarcasm detection using basic ML approaches like Naïve Bayes and SVM. Understanding the importance of each model and its merits and combining them accordingly. This work majorly aims at building a hybrid model which leads to better accuracy which will help readers for better decision making.
Index Terms: Naïve Bayes, SVM, SMO, Hybrid, Accuracy, Sarcasm, Sentiment, Opinion.

Scope of the Article: Classification