An improved semi-supervised dimensionality reduction using feature weighting: Application to sentiment analysis

An improved semi-supervised dimensionality reduction using feature weighting: Application to... •A semi-supervised feature extraction combines with feature weighting is proposed. •Feature weighting considers both co-occurrence of terms and label of documents. •The polarity scores defined in SentiWordNet are reflected in the feature weights. •Six datasets are used to validate the enhanced performance of the proposed method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Expert Systems with Applications Elsevier

An improved semi-supervised dimensionality reduction using feature weighting: Application to sentiment analysis

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0957-4174
D.O.I.
10.1016/j.eswa.2018.05.023
Publisher site
See Article on Publisher Site

Abstract

•A semi-supervised feature extraction combines with feature weighting is proposed. •Feature weighting considers both co-occurrence of terms and label of documents. •The polarity scores defined in SentiWordNet are reflected in the feature weights. •Six datasets are used to validate the enhanced performance of the proposed method.

Journal

Expert Systems with ApplicationsElsevier

Published: Nov 1, 2018

References

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