基于卷积神经网络和Tree-LSTM的微博情感分析  

Sentiment analysis of micro-blog based on CNN and Tree-LSTM

作　　者：王文凯 王黎明[1] 柴玉梅[1] Wang Wenkai;Wang Liming;Chai Yumei(School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China)

机构地区：[1]郑州大学信息工程学院,郑州450001

出　　处：《计算机应用研究》2019年第5期1371-1375,共5页Application Research of Computers

基　　金：社会媒体文本情感可视计算方法研究基金资助项目(U1636111)

摘　　要：微博情感分析旨在研究用户关于热点事件的情感观点,研究表明深度学习在微博情感分析上具有可行性。针对传统卷积神经网络进行微博情感分析时忽略了非连续词之间的相关性,为此将注意力机制应用到卷积神经网络(CNN)模型的输入端以改善此问题。由于中文微博属于短文本范畴,卷积神经网络前向传播过程中池化层特征选择存在丢失过多语义特征的可能性,为此在卷积神经网络的输出端融入树型的长短期记忆神经网络(LSTM),通过添加句子结构特征加强深层语义学习。在两种改进基础上构造出一种微博情感分析模型(Att-CTL),实验表明该模型在微博情感分析上具有优良的特性,尤其在极性转移方面仍保持较高的F_1值。Micro-blog sentiment analysis aims to study the emotional views of users on hot events, and research shows that deep learning is feasible in micro-blog’s sentiment analysis. In view of traditional convolutional neural network, micro-blog sentiment analysis ignores the correlation between discontinuous words. Therefore, this paper applied attention mechanism to the input end of convolutional neural network (CNN) model to improve this problem. Because Chinese micro-blog belongs to the short text category, there was a possibility of losing too many semantic features in the selection of pooling layer features in the process of convolutional neural network forward propagation, so into the long short term memory neural network tree at the output of the convolutional neural network terminal (LSTM), by adding the sentence structure to strengthen the deep semantic learning. Based on the two improvements, it constructed a Chinese micro-blog sentiment analysis model (Att-CTL). Experiments show that the model has excellent characteristics in Chinese micro-blog sentiment analysis, especially in polarity shif- ting , and maintains a high F 1 value.

关 键 词：卷积神经网络 注意力机制 长短期记忆神经网络 微博情感分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]