Detecting the target of sarcasm is hard: Really??

Abstract Sarcasm target detection (identifying the target of mockery in a sarcastic sentence) is an emerging field in computational linguistics. Although there has been some research in this field, accurately identifying the target still remains problematic especially when the target of mockery is not presented in the text. In this paper, we propose a combination of a machine learning classifier and a deep learning model to extract the target of sarcasm from the text. First, we classify sarcastic sentences using machine learning, to determine whether a sarcastic sentence contains a target. Then we use a deep learning model from Aspect-Based Sentiment Analysis to extract the target. Our proposed system is evaluated on three publicly available data sets: sarcastic book snippets, sarcastic tweets, and sarcastic Reddit comments. Our evaluation results show that our approach achieves equal or better performance compared to the current state-of-the-art system, with an 18% improvement on the Reddit data set and similar scores on the Books and Tweets data sets. This is because our method is able to accurately identify when the target of sarcasm is not present. The primary challenge we identify, that is hindering the creation of a high accuracy classifier, is the lack of consistency among human annotators in identifying the target of sarcasm within standard ground-truth data sets.