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A Percentile Transition Ranking Algorithm Applied to Binarization of Continuous Swarm Intelligence Metaheuristics Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Sarcasm Detection Using Features Based on Indicator and Roles

Authors : Satoshi Hiai, Kazutaka Shimada

Published in: Recent Advances on Soft Computing and Data Mining

Publisher: Springer International Publishing

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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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Footnotes
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Metadata
Title
Sarcasm Detection Using Features Based on Indicator and Roles
Authors
Satoshi Hiai
Kazutaka Shimada
Copyright Year
2018
Book
Recent Advances on Soft Computing and Data Mining

Print ISBN: 978-3-319-72549-9

Electronic ISBN: 978-3-319-72550-5

Copyright Year: 2018

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

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