Evaluation of LibSVM and mutual information matching classifiers for multi-domain sentiment analysis

Sun, F; Belatreche, A; Coleman, SA; McGinnity, TM; Li, Y

Abstract

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%.

Citation

Sun, F., Belatreche, A., Coleman, S., McGinnity, T., & Li, Y. (2012, September). Evaluation of LibSVM and mutual information matching classifiers for multi-domain sentiment analysis. Presented at The 23rd Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Ireland

• Presentation Conference Type: Other
• Conference Name: The 23rd Irish Conference on Artificial Intelligence and Cognitive Science
• Conference Location: Dublin, Ireland
• Start Date: Sep 17, 2012
• End Date: Sep 19, 2012
• Publication Date: Sep 1, 2012
• Deposit Date: Jul 27, 2015
• Related Public URLs: http://aics2012.computing.dcu.ie/
• Additional Information: Event Type : Conference