EMOTION AND SARCASM IDENTIFICATION OF POSTS FROM FACEBOOK DATA USING A HYBRID APPROACH

Facebook has become the most important source of news and people's feedback and opinion about almost every daily topic. Facebook represents one of the largest and most dynamic datasets of user generated content. Facebook posts can express opinions on different topics. With this massive amount of information in Facebook, there has to be an automatic tool that can categorize these information based on emotions. The proposed system is to develop a prototype that help to come to an inference about the emotions of the posts namely anger, surprise, happy, fear, sorrow, trust, anticipation and disgust with three sentic levels in each. This helps in better understanding of the posts when compared to the approaches which senses the polarity of the posts and gives just their sentiments i.e., positive, negative or neutral. The posts handling these emotions might be sarcastic too. When detecting sarcasm in social media posts, the various features that are especially inherent to Facebook must be considered with importance.