基于读者视角的文本情感分类

Text Emotion Classification from Reader Perspective

从读者的角度对文本情感进行分类.训练样本集以新闻文章作为样本实例,以文章后读者的投票信息作为样本类别标注的先验知识.针对该不完备的数据集提出了一种半监督学习的分类模型,分类方法采用朴素贝叶斯分类法和EM算法相结合.实验证明该方法不仅简单有效,而且具有较高的分类性能.

The past research of text emotion classification mainly face to the author's emotion .But this article classify texts'emotion from the reader's perspective .Using news articles as training sample sets ,and using readers'vote information behind news articles as prior knowledge of the sample category .we put forward a semi-supervised classification model according to the incomplete data sets ,the classification method using naive bayesian method and in couple with EM algorithm .Experiments show that our method is not only simple and effective ,and has higher classification performance .