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Sarcasm Detection Using Features Based on Indicator and Roles

  • Satoshi Hiai
  • Kazutaka Shimada
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)

Abstract

Sarcasm is a non-literalistic expression and presents a negative meaning with positive expressions. Sarcasm detection is a significant challenge for sentiment analysis which is to analyze documents with opinions. In this study, we propose a method of sarcasm detection on Twitter. We focus on two kinds of feature words. One is words modified by the indicator “
”. The other is words expressing a role. First, we extract these words from tweets. Next, our method uses the lists of these words for a machine learning approach to detect sarcastic tweets. The lists of extracted words are used as features in our method. In the experiment, we compare our method with a baseline based on the features in previous studies. The experimental result shows the effectiveness of our method.

Keywords

Sarcasm Sentiment analysis Opinion mining Microblogging Classification 

Notes

Acknowledgements

This work was partially supported by JSPS KAKENHI Grant Number 17H01840.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Artificial IntelligenceKyushu Institute of TechnologyFukuokaJapan

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