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Sarcasm Detection Using RNN with Relation Vector

Sarcasm Detection Using RNN with Relation Vector

Satoshi Hiai, Kazutaka Shimada
Copyright: © 2019 |Volume: 15 |Issue: 4 |Pages: 13
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781522564218|DOI: 10.4018/IJDWM.2019100104
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MLA

Hiai, Satoshi, and Kazutaka Shimada. "Sarcasm Detection Using RNN with Relation Vector." IJDWM vol.15, no.4 2019: pp.66-78. http://doi.org/10.4018/IJDWM.2019100104

APA

Hiai, S. & Shimada, K. (2019). Sarcasm Detection Using RNN with Relation Vector. International Journal of Data Warehousing and Mining (IJDWM), 15(4), 66-78. http://doi.org/10.4018/IJDWM.2019100104

Chicago

Hiai, Satoshi, and Kazutaka Shimada. "Sarcasm Detection Using RNN with Relation Vector," International Journal of Data Warehousing and Mining (IJDWM) 15, no.4: 66-78. http://doi.org/10.4018/IJDWM.2019100104

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Abstract

Sarcasm detection has been treated as a task that classifies text as sarcastic or non-sarcastic. Sarcasm detection is a significant challenge for sentiment analysis because sarcasm involves a positive expression with a negative meaning. Surface information in text is commonly used as a classification feature. However, the authors must consider both surface and non-surface features. In this article, the authors focus on relation information between pairs of role expressions, such as “boss and staff,” and propose a sarcasm detection method based on surface and relation information. First, the authors extract role pairs from a corpus. Then, the authors construct a relation vector generated from these role pairs and incorporate the relation vector into a recurrent neural network model. The authors evaluated the proposed method by comparing it to previously proposed methods. The results demonstrate the effectiveness of introducing the relation vector to sarcasm detection.

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