吉首大学学报(自然科学版) ›› 2024, Vol. 45 ›› Issue (1): 24-29.DOI: 10.13438/j.cnki.jdzk.2024.01.005

• 计算机 • 上一篇    下一篇

基于卷积神经网络的特定目标文本情感分析模型

叶海燕   

  1. (巢湖学院计算机与人工智能学院,安徽 巢湖 238000)
  • 出版日期:2024-01-25 发布日期:2024-01-31
  • 作者简介:叶海燕(1983—),女,安徽巢湖人,巢湖学院计算机与人工智能学院讲师,硕士生,主要从事物联网工程、情感计算和深度学习研究.
  • 基金资助:
    安徽省质量工程省级教学研究一般项目(2020JYXM1253);安徽省省级教学示范课程(2020SJJXSFK1720);安徽高校自然科学研究重点项目(KJ2020A0681)

Sentiment Analysis Model of Specific Target Text Based on Convolutional Neural Network

YE Haiyan   

  1. (College of Information and Artificial Intelligence Engineering,Chaohu University,Chaohu 238000,Anhui China)
  • Online:2024-01-25 Published:2024-01-31

摘要:在特定目标文本情感分析过程中,文本序列分类受标注方式的限制,导致分析结果的准确率和召回率较低.为了解决这个问题,构建了基于卷积神经网络的特定目标文本情感分析模型(文本分析模型).根据情感差异分析特定目标文本序列,在输入层将文本特征矩阵作为卷积神经网络语言模型的输入数据,拼接成词性序列矩阵;分段池化捕获文本序列不同的关键特征,并分类处理提取到的特征向量;加入dropout机制完成特定目标文本情感分类,确定文本中每个词的重要度信息,实现特定目标文本情感分析.实验结果表明,文本分析模型的准确率高于84%,召回率最大值为87%,能够有效实现特定目标文本情感分析.

关键词: 卷积神经网络, 特定目标, dropout机制, 文本情感

Abstract: In the process of emotion analysis of specific target text,text sequence classification is limited by the labeling method,resulting in low accuracy and recall of analysis results,so a model of emotion analysis of specific target text based on convolution neural network is constructed.Specific target text sequences are analysed based on emotional differences,and the text feature matrix is used as input data to construct a convolutional neural network language model in the input layer,concatenating it into a part of speech sequence matrix.Segmented pooling captures different key features of text sequences and classifies and processes the extracted feature vectors.A dropout mechanism is added to complete sentiment classification of specific target texts,determine the importance information of each word in the text,and achieve sentiment analysis of specific target texts.The experimental results show that the accuracy of the model is higher than 84%,and the maximum recall rate is 87%,which can effectively achieve the emotional analysis of specific target text.

Key words: convolution neural network, specific objectives, dropout mechanism, text emotion

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