融合多头注意力和ConvBiLSTM的文本情感分析OA
Text Sentiment Analysis Integrating Multi-head Attention and ConvBiLSTM
为了更好地预测和理解公众情绪和舆情发展走向,本文分别利用卷积神经网络和长短期记忆网络捕捉文本的局部特征和全局特征,通过多头注意力机制加强模型对关键信息的关注,探究表情符号对文本分类的影响,并构建出对网络舆情进行高效处理的情感分析模型ECBL-MHA.实验结果表明,ECBL-MHA模型在对文本进行分类预测的准确率达到了90.51%,具备应用于情感分析的可行性.
In order to better predict and understand the development trend of public emotions and public opinion,this paper uses CNN and BiLSTM to capture local and global features of text,strengthens the model's attention to key information through multi head attention mechanism,explores the impact of emoticons on text classification,and constructs an efficient sentiment analysis model ECBL-MHA for online public opinion processing.The experimental results show that the ECBL-MHA model achieves an accuracy of 90.51%in text classification prediction,which is feasible for application in sentiment analysis.
王彬彬;李晓晔
齐齐哈尔大学计算机与控制工程学院 黑龙江 齐齐哈尔 161000
计算机与自动化
卷积神经网络长短期记忆网络多头注意力机制表情符号情感分析
CNNBiLSTMMulti-Head AttentionEmojiSentiment Analysis
《福建电脑》 2024 (004)
20-24 / 5
本文得到黑龙江省省属高等学校基本科研业务费科研项目(No.145209124)资助.
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