ICTACT Journal on Soft Computing

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Sarcasm Detection in Online Review Text


Affiliations
1 Division of Computer Engineering, Netaji Subhas Institute of Technology, India
     

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Sarcasm is a type of sentiment where people express negative sentiment using positive connotation words in text and vice-versa. In this work, we propose a cross-domain sarcasm detection framework that allows acquisition, storage and processing of tweets for detecting sarcastic content in online reviews. We conduct our experiments on Amazon product review dataset namely the Sarcasm Corpus Version1 having about 2000 reviews. We use Support Vector Machines (SVM) and Neural Networks (NN) for detecting sarcasm using lexical, pragmatic, linguistic incongruity and context incongruity features. We report the results and present a comparative evaluation of SVM and NN classifiers for single domain sarcasm detection indicating their suitability for the task. Then, we use these models for cross-domain sarcasm detection. The experimental results indicate the reliability of our approach.

Keywords

Sarcasm, Machine Learning, Support Vector Machines, Neural Network Classifier, Amazon, Twitter.
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  • Sarcasm Detection in Online Review Text

Abstract Views: 306  |  PDF Views: 3

Authors

Srishti Sharma
Division of Computer Engineering, Netaji Subhas Institute of Technology, India
Shampa Chakraverty
Division of Computer Engineering, Netaji Subhas Institute of Technology, India

Abstract


Sarcasm is a type of sentiment where people express negative sentiment using positive connotation words in text and vice-versa. In this work, we propose a cross-domain sarcasm detection framework that allows acquisition, storage and processing of tweets for detecting sarcastic content in online reviews. We conduct our experiments on Amazon product review dataset namely the Sarcasm Corpus Version1 having about 2000 reviews. We use Support Vector Machines (SVM) and Neural Networks (NN) for detecting sarcasm using lexical, pragmatic, linguistic incongruity and context incongruity features. We report the results and present a comparative evaluation of SVM and NN classifiers for single domain sarcasm detection indicating their suitability for the task. Then, we use these models for cross-domain sarcasm detection. The experimental results indicate the reliability of our approach.

Keywords


Sarcasm, Machine Learning, Support Vector Machines, Neural Network Classifier, Amazon, Twitter.

References