With the rise of social networking epoch, there has been a surge of user-generated content. Micro-blogging sites have millions of people sharing their thoughts daily because of its characteristic short and simple manner of expression. We propose and investigate a paradigm to mine the sentiment from a popular real-time micro-blogging service, Twitter, where users post real-time reactions to and opinions about everything. In this project, we expound a hybrid approach using both corpus-based and dictionary-based methods to determine the semantic orientation of the opinion words in tweets. We know that there are almost 111 microblogging sites. Microblogging websites are nothing but social media site to which user makes short and frequent posts. Twitter is one of the famous microblogging services where the user can read and post messages which are 148 characters in length. Twitter messages are also called as Tweets. We will use these tweets as raw data. We will use a method that automatically extracts tweets into positive, negative or neutral sentiments. By using the sentiment analysis the customer can know the feedback about the product or services before making a purchase.