%0 Journal Article
%T 结合新闻和评论文本的读者情绪分类方法<br>Reader emotion classification with news and comments
%A 严倩
%A 王礼敏
%A 李寿山
%A 周国栋
%J 山东大学学报（理学版）
%D 2018
%R 10.6040/j.issn.1671-9352.1.2017.003
%X 摘要： 新闻和评论文本是进行读者情绪分类的重要资源,但仅仅使用新闻和文本或者把2类文本进行混合作为一组总体特征,不能充分利用不同文本特征间的区别和联系。基于此,提出了一种双通道LSTM(long short-term memory)方法,该方法把2类文本作为2组特征,分别用单通道LSTM神经网络学习这2组特征文本得到文本的LSTM表示,然后通过联合学习的方法学习这2组特征间的关系。实验结果表明,该方法能有效提高读者情绪的分类性能。<br>Abstract: The news and comments are important resources to classify the reader emotion. However, previous studies only used news texts or mixed two types of texts as a general feature, which did not make the best use of the differences and connections between different textual features. Based on it, the paper proposed a new approach named dual-channel LSTM, which treated two types of texts as different features. First, the approach learned a LSTM representation with a LSTM recurrent neural network. Then, it proposed a joint learning method to learn the relationship between the features. Empirical studies demonstrate the effectiveness of the proposed approach to reader emotion classification
%K 联合学习
%K 读者情绪分类
%K 双通道LSTM
%K &lt
%K br&gt
%K reader emotion classification
%K joint learning
%K dual-channel LSTM
%U http://lxbwk.njournal.sdu.edu.cn/CN/10.6040/j.issn.1671-9352.1.2017.003