Sarcastic Sentiment Detection Based on Types of Sarcasm Occurring in Twitter Data

In Natural Language Processing (NLP), sarcasm analysis in the text is considered as the most challenging task. It has been broadly researched in recent years. The property of sarcasm that makes it harder to detect is the gap between the literal and its intended meaning. It is a particular kind of sentiment which is capable of flipping the entire sense of a text. Sarcasm is often expressed verbally through the use of high pitch with heavy tonal stress. The other clues of sarcasm are the usage of various gestures such as gently sloping of eyes, hands movements, shaking heads, etc. However, the appearances of these clues for sarcasm are absent in textual data which makes the detection of sarcasm dependent upon several other factors. In this article, six algorithms were proposed to analyze the sarcasm in tweets of Twitter. These algorithms are based on the possible occurrences of sarcasm in tweets. Finally, the experimental results of the proposed algorithms were compared with some of the existing state-of-the-art.