A hybrid content and context-based method for sarcasm detection

With the growing use of social media, figurative language has become very common on social media platforms. Given its complexity, figurative language can confuse natural language processing systems and lead to incorrect results. To address this issue, researchers have developed methods to detect humor, jokes, irony, and especially sarcasm. To date, most studies have used deep learning methods to identify sarcasm. Some studies have also incorporated context such as previous posts or conversations to improve the accuracy of sarcasm detection. But the context that can be highly effective in detecting the sarcasm of posts is the characteristics of the writer of the posts. So, the present paper aims to develop a hybrid approach that combines content and context features to better identify sarcastic posts. i.e., this study additionally proposes a deep learning method to model the content of tweets and suggests a multi-dimensional method that considers the user’s writing style and personality traits as context features. Several experiments were used to evaluate the effectiveness of the proposed method. The results indicated that the proposed method outperformed baseline methods in sarcasm detection.

Keywords:

social media , sarcasm detection , deep Learning , content-based features , context-based features