Performance Evaluation of Sentiment Mining Classifiers on Balanced and Imbalanced Dataset

Abstract

The transition from Web 2.0 to Web 3.0 has resulted in creating the dissemination of social communication without limits in space and time. Sentiment analysis has really come into its own in the past couple of years. It’s been a part of text mining technology for some time, but with the rise in social media popularity, the amount of unstructured textual data that can be used as a machine learning data source, is enormous. Marketers use this data as an intelligent indicator for customer preferences. This paper aims to evaluate the performance of sentiment mining classifiers for problems of unbalanced and balanced large data sets for three different products. The classifiers used for sentiment mining in this paper are Support Vector Machine (SVM), Naïve bayes and C5.The results shows that the performance of the classifiers depends on the class distribution in the dataset . Also balanced data sets achieve better results than unbalanced datasets in terms of overall misclassification rate.