Abstract
This paper presents an evidential fusion approach for sentiment classification tasks and a comparative study with linear sum combination. 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 individuals 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Feldnan, R.: Techniques and Applications for Sentiment Analysis. Communications of the ACM 56(4), 82–89 (2013)
Bi, Y., Guan, J.W., Bell, D.: The combination of multiple classifiers using an evidential approach. Artificial Intelligence 17, 1731–1751 (2008)
Denoeux, T.: Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence. Artificial Intelligence 172(2-3), 234–264 (2008)
Smets, P., Kennes, R.: The transferable belief model. Artificial Intelligence 66, 191–243 (1994)
Shafer, G.: A Mathematical Theory of Evidence, 1st edn. Princeton University Press, Princeton (1976)
Bi, Y.: An Efficient Triplet-based Algorithm for Evidential Reasoning. International Journal of Intelligent Systems 23(4), 1–34 (2008)
Srivastava, R.: Alternative form of Dempster’s rule for binary variables. International Journal of Intelligent Systems 20(8), 789–797 (2005)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, San Francisco (2011)
Kim, S., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Sydney, Australia, pp. 423–430 (July 2006)
Internet Movie Database (IMDb) archive, http://reviews.imdb.com/Reviews/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Bi, Y. (2014). Evidential Fusion for Sentiment Polarity Classification. In: Cuzzolin, F. (eds) Belief Functions: Theory and Applications. BELIEF 2014. Lecture Notes in Computer Science(), vol 8764. Springer, Cham. https://doi.org/10.1007/978-3-319-11191-9_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-11191-9_40
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11190-2
Online ISBN: 978-3-319-11191-9
eBook Packages: Computer ScienceComputer Science (R0)