Published Paper Details:

In recent days, invention of new platforms in social media has given lot of boost to the business development. In the business process social media is playing an important role as a deciding factor for success or failure of a business in a growing economy of the country. One such platform which helps people to understand and gauge the business prospectus is twitter. In this paper we are addressing the problem of sentiment analysis in twitter; which mainly deals with classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users - out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange. The aim here is to develop a functional classifier for accurate and automatic sentiment classification of an unknown tweet stream.