Baqapuri, Afroze IbrahimSaleh, SaadIlyas, Muhammad U.Khan, Muhammad MurtazaQamar, Ali Mustafa2017-02-172017-02-172017-02-17201610.1109/ICC.2016.7511391https://infoscience.epfl.ch/handle/20.500.14299/134464WOS:000390993204078This paper addresses the problem of sentiment classification of short messages on microblogging platforms. We apply machine learning and pattern recognition techniques to design and implement a classification system for microblog messages assigning them into one of three classes: positive, negative or neutral. As part of this work, we contributed a dataset consisting of approximately 10, 000 tweets, each labeled on a five point sentiment scale by three different people. Experiments demonstrate a detection rate between approximately 70% and an average false alarm rate of approximately 18% across all three classes. The developed classifier has been made available for online use.text::conference output::conference proceedings::conference paper