Sarcasm Detection Using Role Pairs: Bootstrapping Role Pairs and Weighting

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 negative meaning. Previous work focused on relation information between pairs of role expressions, such as “boss and staff,” and proposed a sarcasm detection method based on surface and relation information. Although they showed the effectiveness of the relation information, due to the small scale of the list of role pairs, the effectiveness was limited to a small scale. In this paper, we attempt to improve the performance of the sarcasm detection method. We propose a role pair extraction method using a bootstrap method to obtain a larger role pair list than the previous work. In addition, we propose weighting methods for each role pair. Then, we evaluate the method using our role pair list and weighting methods by comparing the method using the list of the previous work. We find that we can obtain approximately threefold increase for role pairs. Although the effectiveness is limited, the experimental result shows a potential benefit because the method with topic similarity can reduce the influence of such noise pairs.