基于集成学习的半监督情感分类方法研究

情感分类旨在对文本所表达的情感色彩类别进行分类的任务。该文研究基于半监督学习的情感分类方法,即在很少规模的标注样本的基础上,借助非标注样本提高情感分类性能。为了提高半监督学习能力,该文提出了一种基于一致性标签的集成方法,用于融合两种主流的半监督情感分类方法:基于随机特征子空间的协同训练方法和标签传播方法。首先,使用这两种半监督学习方法训练出的分类器对未标注样本进行标注;其次,选取出标注一致的未标注样本;最后,使用这些挑选出的样本更新训练模型。实验结果表明,该方法能够有效降低对未标注样本的误标注率,从而获得比任一种半监督学习方法更好的分类效果。

Semi-Supervised Sentiment Classification with a Ensemble Strategy

Sentiment classification aims to predict the sentimental orientation expressed in the text. In this paper, we investigate the semi-supervised approaches for sentiment classification in a ensemble learning framework where a abound of unlabeled data is leveraged to enhance the classification performance together with a small amount of labeled data. To improve the performance of the semi-supervised learning approach, we propose a novel ensemble method based on label consistency. Specifically, we combine two popular semi-supervised methodsco-training with random feature subspaces and label propagation to generate the pseudo labeled data for updating the initial labeled data. First, the unlabeled data are labeled by the two semi-supervised learning approaches separately. Then, the unlabeled samples with the consistent labels are considered as pseudo labeled data. Finally, the labeled data is updated with the pseudo labeled data. Experimental study shows that our approach is capable of effectively reducing the error of the pseudo labeled data and thus achieves much better performances than some other approaches for semi-supervised sentiment classification.
Key wordssentiment classification; semi-supervised learning; ensemble learning