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

被引：0
作者：
Rongxuan S. [1 ]
Bin Z. [2 ]
Jianing M. [1 ]
机构：
[1] School of Economics and Management, Harbin Institute of Technology, Harbin
[2] Schoolof Public Administration and Law, Hunan Agricultural University, Changsha
来源：
Data Analysis and Knowledge Discovery | 2022年 / 6卷 / 2-3期
关键词：
Aspect-Level Sentiment Analysis; BRNN; E-Government Application; End to End;
D O I：
10.11925/infotech.2096-3467.2021.0945
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
[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. © 2022, Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：364 / 375
页数：11
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