End-to-End Aspect-Level Sentiment Analysis for E-Government Applications Based on BRNN
Shang Rongxuan1(),Zhang Bin2,Mi Jianing1
1School of Economics and Management, Harbin Institute of Technology, Harbin 150001, China 2Schoolof Public Administration and Law, Hunan Agricultural University, Changsha 410128, China
[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.
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