Sarcasm Detection Using RNN with Relation Vector

Sarcasm Detection Using RNN with Relation Vector

Satoshi Hiai, Kazutaka Shimada
Copyright: © 2019 |Pages: 13
DOI: 10.4018/IJDWM.2019100104
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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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Sarcasm presents a negative meaning using positive expression. The surface sentiment of words in sarcastic text is not always the same as the intended sentiment.

  • Example 1: He is a good boss who gives his staff homework to do on the weekend.

In Example 1, even though the writer uses the positive word “good,” the intention is to criticize the boss. A basic task in sentiment analysis is to classify sentences as positive or negative, and the surface sentiment of words is an important feature for such classification. However, often, sarcastic sentences are misclassified; thus, sarcasm detection is a significant challenge in sentiment analysis. To identify sarcastic sentences, we must consider both surface and non-surface features.

In this study, we focus on relation information as a non-surface feature, and we propose a method to detect sarcastic sentences based on the relations between the target of sarcasm and the person who is dissatisfied with the target. Note that sarcasm typically involves a specific target (Campbell & Katz, 2012). Kumon-Nakamura et al. (1995) claimed that ironic remarks have effect by alluding to a failed expectation. Sarcasm is considered a type of irony. In Example 1, the boss is the target, and the writer criticizes the boss from the perspective of staff who are dissatisfied with the boss’ behavior. The implied expectation that the boss should be a good manager affects the interpretation of sarcasm. To recognize sarcasm in a sentence, the differences in the relation inferred by the role expressions are important features. Consider the following example.

  • Example 2: He is a good teacher who gives his students homework to do on the weekend.

In Example 2, we replace “boss” and “staff” with “teacher” and “students,” respectively. Compared to Example 1, Example 2 is a non-sarcastic sentence because the relationship between teachers and students differs from that of a boss and staff. In this study, we focus on the relationship between two roles, e.g., “boss and staff,” “student and teacher,” and “parent and child.” The proposed method uses this relationship to detect sarcasm. In addition, the proposed method uses a vector to express the relations and uses a Recurrent Neural Network (RNN) to detect sarcastic sentences. The RNN is trained using two types of features, i.e., words in sentences and a vector that expresses the relations between role pairs, e.g., “boss and staff.” We extract role pairs from a corpus to obtain a list of role pairs. We construct the relation vector generated from a role and evaluate the effectiveness of the vector to express the relation of role pairs in a sarcasm detection task.


In previous studies, sarcasm detection has been treated as a task that classifies text as sarcastic or non-sarcastic. Many researchers have used machine learning approaches to classify text. Words or phrases are commonly used as features in machine learning (Davidov et al., 2010; Reyes & Rosso., 2011; Joshi et al., 2015, 2016). However, for sarcasm detection, we must apply both surface and non-surface features. Often, perceiving sarcasm requires background knowledge. For example, to understand that Example 1 is a sarcastic sentence, we must know the relationship between the “boss” and “staff.” In contrast, Example 2 is understood as a non-sarcastic sentence because we have a general understanding of the relationship between the “teacher” and “students.”

Consider the following example, where “boss” and “staff” are replaced by “manager” and “clerks,” respectively.

  • Example 3: He is a good manager who gives clerks homework to do on the weekend.

Here, the relationship between “manager” and “clerks” is similar to the relationship between “boss” and “staffs;” thus, we understand that Example 3 is a sarcastic sentence. The differences and similarities of the relations in a role pair are important to detect sarcasm. Therefore, we use role pair relations to detect sarcastic sentences.

Figure 1.

Overview of the proposed method


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