基于多输入模型及句法结构的中文评论情感分析方法

doi:10.11959/j.issn.2096-0271.2021059

Abstract

Abstract:

Massive network texts have brought huge opportunities and challenges to sentiment analysis tasks.Traditional rule-based methods have been difficult to analyze such texts.Existing deep learning methods have some shortcomings.On the one hand, the inputs of the model only include the text embedding matrix, lack the use of other features.On the other hand, the algorithm of word embedding will lead to the lack of text structure information, then impact the result.Based on the research of syntactic rule in the rule-based sentiment analysis methods, a multi-input model combined with MCNN, LSTM and fully connected neural network was proposed.Meanwhile, a syntactic feature extractor to combine the syntactic features was constructed in the deep learning model.Experiments on three public data sets were conducted.The results show that the model constructed in this article has better classification performance than other models, and the introduction of syntactic rule features has a little improvement in the classification effect of the model.

Key words: sentiment analysis, syntactic rule, multi-input model

CLC Number:

TP183

Baohua ZHANG, Huaping ZHANG, Tieshuai LI, Jianyun SHANG. Chinese comment sentiment analysis method based on multi-input model and syntactic structure[J]. Big Data Research, 2021, 7(6): 41-52.

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References 23

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数据集	总评论数/个	句子最大长度/字符	句子最小长度/字符	平均包含单句数量/个	包含关联词的句子数量/个
Douban	100 000	99	11	4.32	56 998
Hotel	10 000	1 985	4	8.74	8 276
NLPCC2014	10 000	1 004	3	5.48	6 419

模型	Hotel	Douban	NLPCC2014
MCNN_S_LSTM_NN	91.72%	86.42%	76.65%
MCNN_LSMT_NN	91.59%	84.55%	75.49%
MCNN_S_LSTM	91.45%	84.61%	75.21%
MCNN_LSTM	91.01%	84.29%	74.20%
MCNN_NN	90.54%	84.52%	72.54%
S_LSTM_NN	90.51%	83.94%	72.90%
MCNN	90.35%	83.55%	71.25%
S_LSTM	89.90%	83.70%	73.50%
LSTM	88.40%	83.64%	71.55%

模型	Hotel				Douban				NLPCC2014
模型	P	R	F1	ACC	P	R	F1	ACC	P	R	F1	ACC
CNN	88.18%	89.40%	88.78%	88.61%	82.61%	86.17%	84.36%	83.94%	78.32%	67.67%	72.57%	73.56%
LSTM	87.79%	89.70%	88.74%	88.40%	86.01%	80.54%	83.23%	83.64%	75.92%	68.45%	72.00%	72.65%
MCNNALSTM	90.61%	90.79%	90.70%	90.52%	85.38%	84.16%	84.77%	84.16%	74.36%	75.50%	74.93%	73.80%
SLCABG	91.35%	88.91%	90.12%	90.15%	86.13%	83.89%	85.00%	85.11%	79.08%	71.46%	75.08%	75.40%
ATTConv-RNN rand	92.54%	88.42%	90.43%	90.55%	86.19%	84.85%	85.51%	85.55%	81.70%	67.60%	73.98%	75.35%
ABCDM	89.48%	92.67%	91.05%	90.80%	84.20%	86.91%	85.53%	85.22%	78.59%	73.96%	76.20%	76.05%
SAMF-BiLSTM	93.25%	87.52%	90.30%	90.50%	84.72%	85.49%	85.10%	84.96%	74.82%	78.20%	76.47%	75.05%
CNN-BiLSTM	92.32%	88.12%	90.17%	90.30%	86.13%	83.89%	84.77%	84.79%	78.32%	71.07%	74.52%	74.80%
MC-AttCNN-AttBiGRU	91.35%	90.99%	91.17%	91.10%	85.53%	86.80%	86.16%	85.99%	75.99%	78.11%	77.03%	75.85%
MCNN_S_LSTM_NN	90.74%	93.08%	91.87%	91.70%	88.27%	84.17%	86.17%	86.42%	77.56%	77.34%	77.45%	76.65%

Chinese comment sentiment analysis method based on multi-input model and syntactic structure

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