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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (11): 99-107    DOI: 10.11925/infotech.2096-3467.2019.0412
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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.
Key words： Grammar Rules      Sentiment Classification      Bi-LSTM     
Received: 22 April 2019      Published: 18 December 2019
ZTFLH:  TP391  
Corresponding Authors: Zhenfang Zhu     E-mail: zhuzf@sdjtu.edu.cn
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Qiang Lu
Zhenfang Zhu
Fuyong Xu
Qiangqiang Guo

Cite this article:

Qiang Lu,Zhenfang Zhu,Fuyong Xu,Qiangqiang Guo. Chinese Sentiment Classification Method with Bi-LSTM and Grammar Rules. Data Analysis and Knowledge Discovery, 2019, 3(11): 99-107.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0412     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I11/99

参数名 参数值
正向中文情感词汇 11 229
负向中文情感词汇 10 783
否定词 59
程度词 219
实验环境 环境配置
操作系统 Windows7
CPU Intel E5-2640v4 2.40 GHz
内存 4×16GB
编程语言 Python 2.7
分词工具 Jieba 0.39
词嵌入工具 Word2Vec
参数名 参数值
词向量维度 300
隐藏层大小 300
学习率 0.01
Batch_Size 64
L2正则系数 0.001
模型 准确率
RNN 0.726
CNN 0.816
LSTM 0.821
Bi-LSTM 0.845
R-Bi-LSTM 0.912
模型 准确率
Bi-LSTM Model 0.881
Stacked Bi-LSTM Model 0.895
CNN-Bi-LSTM Model 0.901
Bi-LSTM-CRF Model 0.875
R-Bi-LSTM 0.912
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