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- 2018
结合新闻和评论文本的读者情绪分类方法
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Abstract:
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。
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
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