Sarcasm is a sophisticated form of sentiment expression where speaker express their opinions opposite of what they mean. Sarcasm detection and Emotion detection from social net-working sites has been a great field of study. With the growth of e-services such as e-commerce, e-tourism and e-business, the companies are very keen on exploiting emotion and sarcasm analysis for their marketing strategies in order to evaluate the public attitudes towards their brand. Thus efficient emotion and sarcasm modeling system can be a good solution to the above problem. This work aims at developing a system that groups posts based on emotions, sentiment and find sarcastic posts, if present. 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. The Sentiment emotion identification module identifies the sentiment or emotion of the post by evaluating score of each word in the comment which is used by different sarcasm detection methods to detect sarcasm. The emotion identification module uses the lexical databases WordNet, SentiWordNet to find the right sentiment scores for the words with respect to each emotion. It also uses Sarcasm detection algorithms like Emoticon sarcasm detection, Hybrid sarcasm detection, Hashtag Processing, Interjection Word Start (IWT). © 2018 IEEE.