用户评论方面级情感分析研究陈虹，杨燕+，杜圣东西南交通大学信息科学与技术学院，成都611756+通信作者E-mail:***************.cn 摘要：方面级情感分析是自然语言处理的热门研究方向之一，相比于传统的情感分析技术，基于方面的情感分析是细粒度的，能够判断句子中多个目标的情感倾向，能更加准确地挖掘用户对目标的情感极性。
针对以往研究忽略目标单独建模的问题，提出了一种基于双向长短期记忆神经网络（BiLSTM ）的交互注意力神经网络模型（Bi-IAN ）。
该模型通过BiLSTM 对目标和上下文分别进行建模，获得目标和上下文的隐藏表示，提取其中的语义信息。
接下来利用交互注意模块学习上下文和目标之间的注意力，分别生成目标和上下文的表示，捕捉目标和上下文之内和之间的相关性，并重构评价对象和上下文的表示，最终通过非线性层得到分类结果。
在数据集SemEval 2014任务4和Chinese review datasets 上的实验训练显示，在正确率和F 1-score 上，比现有的基准情感分析模型有更好的效果。
关键词：方面级情感分析；深度学习；循环神经网络（RNN ）；注意力机制文献标志码：A中图分类号：TP391.1Research on Aspect-Level Sentiment Analysis of User ReviewsCHEN Hong,YANG Yan +,DU ShengdongSchool of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,ChinaAbstract:Aspect-based sentiment analysis has become one of the hot research directions of natural language pared with the traditional sentiment analysis technology,aspect-based sentiment analysis is aimed at specific targets in sentences,and can judge the sentiment tendency of multiple targets in a sentence,and more accurately mine the sentiment polarity of the target.It is a fine-grained sentiment analysis technology.Aiming at the fact that the previous research ignored the problem of separate modeling of targets,an interactive attention network model based on bidirectional long short-term memory (Bi-IAN)is proposed.The model uses bidirectional long short-term memory (BiLSTM)to model the targets and the context respectively,to obtain hidden representation and extract the semantic information.Next,the attention vector between the context and the targets is learnt through interactive learning,and then the representation of the target and the context are generated.The relevance within and between the target and the context is captured,the representation of the target and context is reconstructed,and finally the model gets the classification result through the non-linear layer.Experimental training on the dataset SemEval 2014task 4and Chinese review datasets shows that the model proposed has better results than the existing benchmark sentiment analysis model in terms of accuracy and F 1-score.Key words:aspect-level sentiment analysis;deep learning;recurrent neural network (RNN);attention mechanism计算机科学与探索1673-9418/2021/15(03)-0478-08doi:10.3778/j.issn.1673-9418.2007011基金项目：国家自然科学基金（61976247）；国家科技支撑计划（2015BAH19F02）。
