基于BRNN的政务APP评论端到端方面级情感分析方法

End-to-End Aspect-Level Sentiment Analysis for E-Government Applications Based on BRNN

【目的】提出一种基于双向循环神经网络(Bidirectional Recurrent Neural Network,BRNN)的端到端方面级情感分析方法,实现了对政务APP评论的细粒度情感分析。【方法】通过搭建一个包含双层BRNN结构以及三个功能模块的神经网络,分别对政务APP评论文本的边界与情感倾向进行识别,同时完成方面实体的抽取。【结果】本文所搭建的基于BRNN的E2E-ALSA模型,具有优秀的拟合与泛化能力,其精确率、召回率与F1值均达到0.93以上。【局限】该模型仅能对显性方面实体进行联合抽取,评论文本的隐性方面抽取仍然需要独立进行;数据集偏小。【结论】通过对政务APP评论文本进行方面实体与情感的联合抽取,可以较好地识别与解释用户对于移动政务系统的情感需求与被满足情况,更精准地挖掘移动政务工作痛点。

[Objective] This paper proposes an end-to-end aspect-level sentiment analysis method based on BRNN, aiming to conduct fine-grained sentiment analysis for reviews of government APPs. [Methods] First, we built a neural network containing a two-layer BRNN structure and three functional modules. Then, we recognized the boundary and sentiment tendency of the government APP reviews, as well as extracted aspect entities.[Results] The proposed E2E-ALSA model had excellent classification and generalization ability. Its precision,recall and F1-score all exceeded 0.93. [Limitations] The model can only jointly extract explicit aspect entities,while the implicit aspect extraction needs to be performed independently. The sample size needs to be expanded.[Conclusions] The proposed method could identify the users’ emotional needs and reactions to the e-government systems.