Classifying twitter data with Naïve Bayes Classifier

Information Classification is the categorization of the huge amount of data in an efficient and useful way. In the current scenario data is growing exponentially due to the rise of internet rich applications. One such source of information is the blogs. Blogs are web logs maintained by their authors that contain information related to a certain topic and also contain authors view about that topic. Micro blogs, on the other hands, are variations of blogs that contain smaller data as compared to blogs. In this project, Twitter, a micro blogging website has been targeted to gather information on certain trending topics. The information is in the form of tweets. A tweet is a post or an update on status on the Twitter website. These tweets are extracted using Twitter Search APIs. This data is then classified into different classes based on its content. Using the classified data, features are extracted from the tweets and suggestions are given to the users based on the trending topics.