摘要
针对消费短文本评论中的情感倾向性分类问题,提出了一种BSP-CNN混合神经网络模型。模型先使用双向简单循环单元(BiSRU)对数据进行特征表示,再使用逐点卷积神经网络(P-CNN)进一步学习语义特征,并输出情感倾向性分类结果。实验结果表明,与传统的长短期记忆神经网络(LSTM)和卷积神经网络(CNN)相比,BSPCNN混合神经网络模型有效简化了计算,缩短了运行时间,并且在不同大小和不同文本长度的数据集上均能取得更高的F1值。
In view of the classification of emotional tendency in the short text comments on consumption, a BSP-CNN hybrid neural network model is proposed. The model first uses the Bidirectional Simple Recurrent Unit(BiSRU)to characterize the data, then uses Point-by-point Convolutional Neural Network(P-CNN)to further learn semantic features and output the results of emotional tendency classification. Experimental results show that compared with traditional Long Short-Term Memory neural networks(LSTM)and Convolutional Neural Networks(CNN), the BSP-CNN hybrid neural network model effectively simplifies calculation, shortens the running time, and obtains higher F1 socre on data sets of different sizes and text lengths.
作者
廖小琴
徐杨
LIAO Xiaoqin;XU Yang(College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China)
出处
《计算机工程与应用》
CSCD
北大核心
2019年第23期120-124,共5页
Computer Engineering and Applications
基金
贵州省科技计划项目(黔科合LH字[2016]7429号)
贵州大学引进人才项目(No.2015-12)
关键词
情感倾向性分析
双向简单循环单元
逐点卷积神经网络
混合神经网络
sentiment orientation analysis
bidirectional simple recurrent unit
point-by-point convolutional neural network
hybrid neural network