Paper Title
Large Scale and Real Time Classifiers for Social Media Text

Abstract
Social media text has received attention of researchers and decision makers due to the valuable information that can be extracted from the text such as people behavior and their opinions. Classification is very important step to convert the raw text to information or knowledge. However, social media text has some serious problems such as, repeated characters, new words, a lot of unwanted symbols and informal structure. Therefore, to standardized the text, regular expression, preprocessing and numerical conversion are applied. Another concern in this paper is to find the best classifier suitable for large scale and real time implementation. The results indicate that the stochastic gradient descent SGD has the best scores and the fastest testing time over the two tested datasets. Keywords - Classification, Large scale, Real time, Social media.