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Received:May 18, 2017 Revised:June 05, 2017
Received:May 18, 2017 Revised:June 05, 2017
中文摘要: 文本情感分类是自然语言处理领域的研究热点，更是产品评价领域的重要任务.考虑到词向量与句向量之间的语义关系和用户信息、产品信息对文本情感分类的影响，提出余弦相似度LSTM网络. 该网络通过在不同语义层级中引入用户信息和产品信息的注意力机制，并根据词向量和句向量之间的相似度初始化词层级注意力矩阵中隐层节点的权重. 在Yelp13、Yelp14和IMDB三个情感分类数据集上的实验结果表明文中方法的有效性.
Abstract:Text sentiment classification is a popular subject of natural language processing and the crucial problem in product evaluation. Based on semantic relationship of word vector and sentence vector and the impact of user information, product information to text sentiment classification, Cosine Similarity Long-Short Term (CSLSTM) network is proposed. CSLSTM considers attention mechanisms of user information and product information in various semantic levels. And it involves a effective initialization method in hidden level weights of word-level attention matrix according to similarity of word vector and sentence vector. The competitive results are derived from three sentiment classification datasets, Yelp13, Yelp 14, and IMDB.
keywords: text sentiment classification attention mechanisms user information product information semantic relationship similarity
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引用文本：
庄丽榕,叶东毅.基于CSLSTM网络的文本情感分类.计算机系统应用,2018,27(2):230-235
ZHUANG Li-Rong,YE Dong-Yi.Text Sentiment Classification Based on CSLSTM Neural Network.COMPUTER SYSTEMS APPLICATIONS,2018,27(2):230-235
庄丽榕,叶东毅.基于CSLSTM网络的文本情感分类.计算机系统应用,2018,27(2):230-235
ZHUANG Li-Rong,YE Dong-Yi.Text Sentiment Classification Based on CSLSTM Neural Network.COMPUTER SYSTEMS APPLICATIONS,2018,27(2):230-235
