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Chinese Sentiment Classification Method with Bi-LSTM and Grammar Rules |
Qiang Lu,Zhenfang Zhu(),Fuyong Xu,Qiangqiang Guo |
School of Information Science and Electrical Engineering, Shandong Jiaotong University, Ji’nan 250357, China |
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Abstract [Objective] This paper proposes a new classification method based on grammar rules, aiming to improve the accuracy of sentiment analysis for Chinese texts. [Methods] Firstly, we combined the Chinese grammar rules with Bi-LSTM in the form of constraints and standardized the adjacent positions of sentences from the experimental corpus. Then, we generated the linguistic functions of non-emotional, emotional, negative, and degree words at sentence level. [Results] Compared with the RNN, LSTM and Bi-LSTM models, the accuracy of our model reached upto 91.2%. [Limitations] The experimental data was only collected from the hotel reviews. More research is needed to examine the performance of this model on other data sets. [Conclusions] The proposed method improves the accuracy of sentiment classification for Chinese.
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Received: 22 April 2019
Published: 18 December 2019
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Corresponding Authors:
Zhenfang Zhu
E-mail: zhuzf@sdjtu.edu.cn
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