Sentiment Analysis is a technique to identify people’s opinion, attitude, sentiment, and emotion towards any specific target such as individuals, events, topics etc. And the sarcasm is a special kind of sentiment that comprise of words which mean the opposite of what you really want to say. It is largely used in social networks like facebook and twitter where people mock or criticize in a way that makes it difficult even for humans to tell if what is said is what is meant. Therefore in order to improve automatic sentiment analysis of data collected from social networks, recognizing sarcastic statements is a mandatory. In this paper, we try to characterize a sarcasm recognizer to identify tweets that is in the most common form of sarcasm which consists of a positive sentiment contrasted with a negative situation. For example, many sarcastic tweets include a positive sentiment, such as “love” or “enjoy”, followed by an expression that describes an undesirable activity or state (e.g., “taking exams” or “being ignored”). We present an approach that automatically learns lists of positive sentiment phrases and negative situation phrases from sarcastic tweets. Then we try to classify new tweets using this kind of contrasting contexts. We show that identifying contrasting contexts using the learned phrases yields promising results for sarcasm recognition task.

Keywords—Natural language processing, Sarcasm Detection, Irony Detection, Satire Detection, Tweets, Twitter, Opinion mining, Parsing, POS tagging, Sentiment, #Sarcasm, #Irony, #Satire ,#ToSumUp, #HardWork.