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    基于情感特征聚类的半监督情感分类

    Semi-Supervised Sentiment Classification Based on Sentiment Feature Clustering

    • 摘要: 情感分类是观点挖掘的一个重要的方面.提出了一种基于情感特征聚类的半监督式情感分类方法,该方法只需要对少量训练数据实例进行情感类别标注.首先从消费者评论中提取普通分类特征和情感特征,普通分类特征可以用来训练一个情感分类器.然后使用spectral聚类算法把这些情感特征映射成扩展特征.普通分类特征和扩展特征一起通过训练得到另一个情感分类器.2个分类器再从未标签数据集中选择实例放入到训练集合中,并通过训练得到最终的情感分类器.实验结果表明,在同样的数据集上该方法的情感分类准确度比基于self-learning SVM的方法和基于co-training SVM的方法的情感分类准确度要高.

       

      Abstract: Sentiment classification for text is an important aspect of opinion mining. This paper proposes a semi-supervised sentiment classification method based on sentiment feature clustering. The method only requires a small number of labeled training data instances. Firstly, the method extracts common text features and sentiment features. Common text features can be used to train the first sentiment classifier. Then the spectral clustering-based algorithm is employed to map sentiment features into extended features. The extended features and common text features are combined together to form the second sentiment classifier. The two classifiers select instances from the unlabeled dataset into the training dataset to train the final sentiment classifier. Experimental results show that the proposed method can reach higher sentiment classification accuracy than both the self-learning SVM-based method and the co-training SVM-based method.

       

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