Using Arbiter and Combiner Tree to Classify Contexts of Data

This paper reports on the use of ensemble learning to classify as either positive or negative the sentiment of Tweets. Tweets were chosen as Twitter is a popular tool and a public, human annotated dataset was made available as part of the SemEval 2013 competition. We report on a classification approach that contrasts single machine learning algorithms with a combination of algorithms in an ensemble learning approach. The single machine learning algorithms used were support vector machine (SVM) and Naïve Bayes (NB), while the methods of ensemble learning include the arbiter tree and the combiner tree. Our system achieved an F-score using Tweets and SMS with the arbiter tree at 83.57% and 93.55%, respectively, which was better than base classifiers; meanwhile, the results from the combiner tree achieved lower scores than base classifiers