Self-Deprecating Sarcasm Detection: An Amalgamation of Rule-Based and Machine Learning Approach

Sarcasm is a special category of figurative language, which is mainly used in online social media to convey messages with implicit semantics and criticism. Such messages are used for sarcastic remarks using contemptuous, ridicule, bitter, taunt, and mock related words or phrases. Though sarcasm detection is a well-considered problem by the researchers, to the best of our knowledge, none of them has considered the problem of self-deprecating sarcasm, which is a special category of sarcasm, mainly used by the users to deprecate or criticize themselves using sarcastic phrases. In this paper, we propose a novel self-deprecating sarcasm detection approach using an amalgamation of rule-based and machine learning techniques. The rule-based techniques aim to identify candidate self-around tweets, whereas machine learning techniques are used for feature extraction and classification. A total number of 11 features, including six self-deprecating features and five hyperbolic features are identified to train three different classifiers - decision tree, naïve Bayes, and bagging. The proposed approach is evaluated over a Twitter dataset containing 107536 tweets, and compared with some state-of-the-art methods for sarcasm detection.