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International Conference on Soft Computing and Data Mining

SCDM 2018: Recent Advances on Soft Computing and Data Mining pp 418–428Cite as

Sarcasm Detection Using Features Based on Indicator and Roles

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Part of the book series: Advances in Intelligent Systems and Computing ((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.

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Notes

  1. 1.

    http://www.macmillandictionary.com.

  2. 2.

    http://pj.ninjal.ac.jp/corpus_center/bccwj.

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Artificial Intelligence, Kyushu Institute of Technology, 680-4 Iizuka, Fukuoka, 820-8502, Japan

    Satoshi Hiai & Kazutaka Shimada

Authors
  1. Satoshi Hiai
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  2. Kazutaka Shimada
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Corresponding author

Correspondence to Satoshi Hiai .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Rozaida Ghazali

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Mustafa Mat Deris

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Nazri Mohd Nawi

  4. School of Information Technology, Deakin University, Geelong, Victoria, Australia

    Jemal H. Abawajy

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Hiai, S., Shimada, K. (2018). Sarcasm Detection Using Features Based on Indicator and Roles. In: Ghazali, R., Deris, M., Nawi, N., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2018. Advances in Intelligent Systems and Computing, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-319-72550-5_40

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  • DOI: https://doi.org/10.1007/978-3-319-72550-5_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72549-9

  • Online ISBN: 978-3-319-72550-5

  • eBook Packages: EngineeringEngineering (R0)

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