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Evidential Fusion for Sentiment Polarity Classification

Bi, Yaxin (2014) Evidential Fusion for Sentiment Polarity Classification. In: 3rd International Conference on Belief Functions 2014, Oxford. Springer. Vol 8764 9 pp. [Conference contribution]

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URL: http://www.informatik.uni-trier.de/~ley/pers/hd/b/Bi:Yaxin

Abstract

This paper presents an evidential fusion approach for senti- ment classification tasks and a comparative study with linear sum com- bination. It involves the formulation of sentiment classifier output in the triplet evidence structure and adaptation of combination formulas for combining simple support functions derived from triplet functions by using Smets’s rule, the cautious conjunctive rules and linear sum rule. Empirical comparisons on the performance have been made in individ- uals and in combinations by using these rules, the results demonstrate that the best ensemble classifiers constructed by the four combination rules outperform the best individual classifiers over two public datasets of MP3 and Movie-Review.

Item Type:Conference contribution (Paper)
Keywords:Belief functions, combination rules, linear sum and senti- ment polarity classification.
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:30731
Deposited By: Dr Yaxin Bi
Deposited On:21 Jan 2015 15:30
Last Modified:21 Jan 2015 15:30

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