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Sentiment Classification by Combining Triplet Belief Functions

Bi, Yaxin, Mulvenna, Maurice and Jurek, Anna (2014) Sentiment Classification by Combining Triplet Belief Functions. In: International Conference on Knowledge Science, Engineering and Management 2014, Romania. Springer. Vol 8793 12 pp. [Conference contribution]

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URL: http://link.springer.com/chapter/10.1007%2F978-3-319-12096-6_21

Abstract

Sentiment analysis is an emerging technique that caters for semantic orientation and opinion mining. It is increasingly used to anal- yse online product reviews for identifying customers’ opinions and atti- tudes to products or services in order to improve business performance of companies. This paper presents an innovative approach to combining outputs of sentiment classifiers under the framework of belief functions. The approach is composed of the formulation of outputs of sentiment classifiers in the triplet structure and adoption of its formulas to combin- ing simple support functions derived from triplet functions by evidential combination rules. The empirical studies have been conducted on the performance of sentiment classification individually and in combination, the experimental results show that the best combined classifiers made by these combination rules outperform the best individual classifiers over the MP3 and Movie-Review datasets.

Item Type:Conference contribution (Paper)
Keywords:Sentiment analysis, opinion mining, triplet belief functions and combination rules
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:30732
Deposited By: Dr Yaxin Bi
Deposited On:21 Jan 2015 15:29
Last Modified:21 Jan 2015 15:29

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