Sarcasm Detection Using RNN with Relation Vector

Satoshi Hiai and Kazutaka Shimada
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Satoshi Hiai: Kyushu Institute of Technology, Iizuka, Japan
Kazutaka Shimada: Kyushu Institute of Technology, Iizuka, Japan

International Journal of Data Warehousing and Mining (IJDWM), 2019, vol. 15, issue 4, 66-78

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.

Date: 2019
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