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Predicting consumer sentiments using online sequential extreme learning machine and intuitionistic fuzzy sets

  • Extreme Learning Machine’s Theory & Application
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Abstract

Predicting consumer sentiments revealed in online reviews is crucial to suppliers and potential consumers. We combine online sequential extreme learning machines (OS-ELMs) and intuitionistic fuzzy sets to predict consumer sentiments and propose a generalized ensemble learning scheme. The outputs of OS-ELMs are equivalently transformed into an intuitionistic fuzzy matrix. Then, predictions are made by fusing the degree of membership and non-membership concurrently. Moreover, we implement ELM, OS-ELM, and the proposed fusion scheme for Chinese reviews sentiment prediction. The experimental results have clearly shown the effectiveness of the proposed scheme and the strategy of weighting and order inducing.

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Acknowledgments

The authors thank the Guest Editors and two anonymous reviewers for their valuable comments and suggestions, which helped improve the paper greatly. This work was supported by the University Science Research Project of Jiangsu Province under Grant 11KJD630001.

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Correspondence to Hai Wang.

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Wang, H., Qian, G. & Feng, XQ. Predicting consumer sentiments using online sequential extreme learning machine and intuitionistic fuzzy sets. Neural Comput & Applic 22, 479–489 (2013). https://doi.org/10.1007/s00521-012-0853-1

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