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

Sun, F, Belatreche, A, Coleman, SA, McGinnity, TM and Li, Y 2012, Evaluation of LibSVM and mutual information matching classifiers for multi-domain sentiment analysis , in: The 23rd Irish Conference on Artificial Intelligence and Cognitive Science, 17-19 September 2012, Dublin, Ireland.

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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%.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Publisher: Artificial Intelligence and Cognitive Science (AICS)
Refereed: Yes
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 27 Jul 2015 10:59
Last Modified: 05 Apr 2016 18:18
URI: http://usir.salford.ac.uk/id/eprint/33114

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