  This project addresses the problem of sentiment analysis in twitter; that is
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 of this
project is to develop a functional classifier for accurate and automatic
sentiment classification of an unknown tweet stream.
