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.

翻译：该项目解决了Twitter上情绪分析的问题;这是根据他们表达的情绪对推特进行分类:正面的、负面的或中立的。Twitter是一个在线微博和社会网络平台,让用户能够写出最长140个字符的简短状况更新。这是一个迅速扩大的服务,有2亿注册用户,其中1亿是活跃用户,其中一半每天在Twitter上登录 — — 每天产生近2.5亿个推特。由于使用量巨大,我们希望通过分析在推特上表达的情绪来反映公众的情绪。分析公众情绪对于许多应用非常重要,例如公司试图在市场上找到产品的反应,预测政治选举,预测证券交易所等社会经济现象。该项目的目的是开发一个功能性分类器,对未知的推特流进行准确和自动的情绪分类。