With the advent of internet, there has been an exponential increase in user created content like customer reviews, comments and opinions. The primary cause for this sudden increase is the rapid adoption of social networks. These websites act as a medium to quickly and effortlessly share content across a wide array of domains such as products, events, people etc. This wealth of user knowledge if processed properly could be very beneficial to various businesses, governments and individuals. But the one crucial step that prevents the use of such data is that most modern techniques that do so are extremely time-consuming. This lead to a desire to develop a system that can automatically and intelligently mine such huge amounts of data and classify according to the positivity or negativity expressed within. Natural Language Processing (NLP), a branch of computer science that deals with the fruitful interpretation of human languages by computers, has given rise to a set of algorithms that perform the automated mining of attitudes, opinions and emotions from text, speech and various other sources, called Sentiment Analysis. Objective of this paper is to compare and contrast the effect of various optimizers on the efficiencies of these algorithms.