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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 |
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Abstract [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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Received: 31 August 2021
Published: 14 April 2022
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Fund:National Social Science Fund of China(17ZDA030) |
Corresponding Authors:
Shang Rongxuan,ORCID:0000-0002-3914-2650
E-mail: 564047413@qq.com
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