People increasingly use Twitter to share advice, opinions, news, moods, concerns, facts, rumors, and everything else imaginable. Much of that data is public and available for mining. However, classifying automatically the sentiment of the Twitter messages into either positive or negative with respect to a query term represents a new research challenge. Variety of approaches that use natural language and statistical techniques failed to report high accuracy of tweets classification due to the nature of these tweets containing large number of abbreviations, emoticons and ill structured grammar. In this article we are presenting a programming approach that uses the Weka data mining APIs to classify tweets. Using this programming approach we can experiment on how to train the classifiers and determine which one is more effective than the others. In our experiments, the K* classifier is found to report a high degree of accuracy in tweets classification.