摘要
针对文本中关键信息被忽略以及分类准确率不高的问题,提出一种加权word2vec的卷积神经网络(CNN)与ATT-BiGRU混合神经网络情感分析模型.由于word2vec生成的词向量无法突出文本关键词的作用,因此引入词频-逆文档频率(TF-IDF)算法计算词汇权重值.然后,将加权运算后的词向量输入CNN与ATT-BiGRU混合模型提取隐含特征.该模型通过卷积神经网络(CNN)和基于注意力机制的双向门限循环单元(ATT-BiGRU)分别提取文本特征,以此来提高文本的表示能力.多组实验对比结果表明,与其他算法相比较,该模型的分类准确率最高且耗费时间代价小.
Aiming at the problem that the key information in the text is ignored and the classification accuracy is not high,a weighted word2vec CNN and ATT-BiGRU mixed neural network sentiment analysis model is proposed.Since the word vector generated by word2vec cannot highlight the role of text keywords,the term frequency-inverse document frequency(TF-IDF)algorithm is introduced to calculate the vocabulary weight value.Then,the weighted operation word vector is input into the mixed model of CNN and ATT-BiGRU to extract the hidden features.The proposed model extracts text features by Convolutional Neural Network(CNN)and attention-based Bidirectional Gated Recurrent Unit(ATT-BiGRU)to improve text representation.Compared with other algorithms,the results show that the classification accuracy of the proposed model is the highest and the cost is small.
作者
刘道华
崔玉爽
冯宸
王莎莎
LIU Daohua;CUI Yushuang;FENG Chen;WANG Shasha(College of Computer and Information Technology;Henan Key Laboratory of Analysis and Applications of Education Big Data,Xinyang Normal University,Xinyang 464000,China)
出处
《信阳师范学院学报（自然科学版）》
CAS
北大核心
2021年第3期472-477,共6页
Journal of Xinyang Normal University(Natural Science Edition)
基金
国家自然科学基金项目(31872704)
河南省教师教育课程改革研究项目(2021-JSJYZD-008)。
关键词
TF-IDF
卷积神经网络
双向门限循环单元
情感分析
TF-IDF
convolutional neural network
bidirectional gated recurrent unit
sentiment analysis
