Over the past years, the amounts of data generated from the Internet services have increased significantly. Innovations in the field of ICT have enabled new business opportunities for creating services capable of handling vast data volumes. Technology has reached the level, where people are interconnected with social media on daily basis and are able to share their life over social networks. Twitter is an important force in today’s world. It’s a media platform that has been popularised in the last decade which lets the user describe their views in 140 characters. A large amount of data is generated which can be manipulated and mined for numerous operations. This project involves classification of tweets into two main sentiments: positive and negative. In this project, the use of features such as unigram, bigram and effects of data pre-processing like stemming is observed. Naive Bayes, Support Vector Machines (SVM) are used as the main classifiers. The idea is to take the tweets using Twitter4J and then pre-process them accordingly. Then these algorithms are applied to classify them on the basis of their sentiments. The major motivation of the project is to compare different Classifier algorithms and perform Data Mining operations on the same.