ANALYSIS OF TWITTER DATA WITH MACHINE LEARNING TECHNIQUES

Classification of data is an important aspect of getting vigorous knowledge and help to analyze and perform any further action. This paper deals with how different Machine-Learning Techniques classify on features of timewindows of Twitter, a micro-blogging social media and to determine whether or not these times-windows are followed by Buzz events. In particular, we compare different machine learning techniques like Naïve Bayes and SVM, to find the accuracy of classification with or without applying dimensional reduction in the number of attributes with the help of PCA algorithms.