Notice of RetractionSubjective relation identification in Chinese opinion mining based on sentential features and ensemble classifier

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Identifying the subjective relationship is important for opinion mining on product reviews in Chinese. The commonly used identification methods adopt classifiers as the identifier. However, it is difficult to maintain high accuracy and high recall rate simultaneously for the instability of exciting classification method. Motivated by this, we present a method based on sentential features and ensemble classifier to identify the subjective relation from product reviews in Chinese. This method derives sentential futures of candidate feature-opinion pairs from text besides traditional features such as lexical, part of speech, semantic and positional features, and builds sub-classifiers using these features. Then it builds an ensemble classifier with weighted voting mechanism to identify the subjective relationship between feature-opinion pairs. Extensive studies on corpus of book and phone reviews in Chinese demonstrate that the introduction of sentential feature could improve the recall rate of classifier, and a weighted ensemble classifier also could achieve the higher value of F-measure with the trade-off between the accuracy and recall rate of sub-classifiers.