计算机工程与应用2019，Vol.55Issue(23)：120-124,5.DOI:10.3778/j.issn.1002-8331.1908-0063
面向BSP-CNN的短文本情感倾向性分类研究
Research on Classification of Short Text Emotional Tendency for BSP-CNN
摘要
Abstract
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 char-acterize 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.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Key words
sentiment orientation analysis/bidirectional simple recurrent unit/point-by-point convolutional neural net-work/hybrid neural network分类
信息技术与安全科学引用本文复制引用
廖小琴,徐杨..面向BSP-CNN的短文本情感倾向性分类研究[J].计算机工程与应用,2019,55(23):120-124,5.基金项目
贵州省科技计划项目(黔科合LH字[2016]7429号) （黔科合LH字[2016]7429号）
贵州大学引进人才项目(No.2015-12). （No.2015-12）
