Abstract: Sentiment analysis and sarcasm detection is a specialized area in the field of Information Retrieval (IR) and Natural Language Processing (NLP). To understand the user’s mood, opinion, attitude, emotion or view in the text, the study of sentiment analysis is required. Sarcasm is a special kind of sentiment where people use positive words to describe their negative feeling. Detection of sarcasm in the text helps to revamp the efficiency of sentiment analysis. In this study, we surpass simple sentiment classification viz. positive, negative and neutral to aim deeper emotion classification of Twitter data, i.e., for identifying the emotions in six discrete categories (anger, fear, disgust, sadness, happiness, surprise) and propose a generalized approach for determining the mood of the user from the Tweets and thereby detecting sarcasm in the text if any. The hybrid approach has been used here is the combination of machine learning and lexicon based approaches.