基于混合词嵌入的双通道注意力网络中文文本情感分析  

Chinese Text Sentiment Analysis Based on Dual Channel Attention Network with Hybrid Word Embedding

作　　者：周宁 钟娜 靳高雅 刘斌 Zhou Ning;Zhong Na;Jin Gaoya;Liu Bin(School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学电子与信息工程学院,兰州730070

出　　处：《数据分析与知识发现》2023年第3期58-68,共11页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金(项目编号:61650207、61963023);兰州交通大学天佑创新团队(项目编号:TY202003)的研究成果之一。

摘　　要：【目的】解决传统静态词向量嵌入方法无法有效处理中文文本中的一词多义问题和上下文情感特征与内部语义关联结构难以挖掘的问题。【方法】在一个通道利用粗糙数据推理将与文本有关的情感元素融入Word2Vec与FastText词向量中,使用CNN提取文本局部特征;在另一个通道使用BERT进行词嵌入补充,利用BiLSTM获取文本全局特征。最后加入注意力计算模块进行双通道特征深层交互。【结果】在三个中文数据集上的实验准确率最高达到92.43%,较基准模型最高值提升0.81个百分点。【局限】所选用的数据集仅针对粗粒度情感分类建模,尚未考虑在细粒度领域的实验。【结论】比较本模型与对比模型实验结果,证明了本模型有效提升了中文文本情感分类的性能。[Objective]This paper addresses the challenges facing the traditional static word vector embedding method,aiming to handle polysemy in Chinese texts effectively.It also excavates the contextual emotional features and internal semantic association structure.[Methods]In one channel,we integrated the sentiment elements related to the text into Word2Vec and FastText word vectors through rough data reasoning.We also used CNN to extract the local features of the text.In the other channel,we employed BERT for word embedding supplement and used BiLSTM to obtain the global features of the texts.Finally,we added the attention calculation module for the deep interaction of dual channel features.[Results]The experiment on three Chinese datasets achieved the highest accuracy of 92.43%,representing an improvement of 0.81%over the best value of the benchmark model.[Limitations]The selected datasets are only for modelling coarse-grained sentiment classification.We did not conduct experiments in the fine-grained domain.[Conclusions]The proposed model could effectively improve the performance of Chinese text sentiment classification.

关 键 词：粗糙数据推理 动态词向量 注意力机制 文本情感分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]