Evaluation of LibSVM and mutual information matching classifiers for multi-domain sentiment analysis
Fan Sun, Ammar Belatreche, Sonya Coleman, T.M. McGinnity, Yuhua Li · University of Salford Institutional Repository (University of Salford) · 2012
This paper addresses the new application of two classifier algorithms, namely LibSVM (ν-SVM) and Mutual Information Matching (MIM), to single and multi-domain sentiment analysis. The aim is to improve the performance of sentiment classification accuracy in multiple domains. Analysis of the performance of the two classifiers shows that the use of LibSVM classifier in multi-domain sentiment analysis performs better than other classification methods (MIM,k-NN, NB and SVM) with a classification accuracy of 94.875%.