Sarcasm Detection Method to Improve Review Analysis

Shota Suzuki, Ryohei Orihara, Yuichi Sei, Yasuyuki Tahara, Akihiko Ohsuga

Abstract

Currently, classifying sarcastic sentences into positive and negative sentiments has been a difficult problem and an important task. The sarcastic sentences could indicate negative meaning by using positive expressions, or positive meaning by using negative expressions. Sarcasm is a special kind of sentiment that comprise of words which mean the opposite of what you really want to say, especially in order to insult or wit someone, to show irritation, or to be funny. Therefore, determining sarcasm is an important task in order to correctly classify the sentence. In this paper, we propose an approach to detect sarcasm. First, we apply dependency parsing to amazon review data. After that, we classify phrases in the sentence into the proposed phrase based on the sequence of part-of-speech as proposed by Bharti et al. After being classified into either one of the phrase types, it is determined whether each phrase is positive or negative. If the emotions of the situation phrases and the sentiment phrases are different, the sentence is determined to be a “sarcasm”. Using the above method, the experimental result shows the effectiveness of our method as compared with the the existing research.

References

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Paper Citation


in Harvard Style

Suzuki S., Orihara R., Sei Y., Tahara Y. and Ohsuga A. (2017). Sarcasm Detection Method to Improve Review Analysis . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 519-526. DOI: 10.5220/0006192805190526


in Bibtex Style

@conference{icaart17,
author={Shota Suzuki and Ryohei Orihara and Yuichi Sei and Yasuyuki Tahara and Akihiko Ohsuga},
title={Sarcasm Detection Method to Improve Review Analysis},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={519-526},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006192805190526},
isbn={978-989-758-220-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Sarcasm Detection Method to Improve Review Analysis
SN - 978-989-758-220-2
AU - Suzuki S.
AU - Orihara R.
AU - Sei Y.
AU - Tahara Y.
AU - Ohsuga A.
PY - 2017
SP - 519
EP - 526
DO - 10.5220/0006192805190526