基于胶囊网络的方面级情感分类研究

doi:10.11959/j.issn.2096-6652.202031

Abstract

Abstract:

Considering the difficulty of judging the mixed multiple sentimental polarities in a text,aspect-level sentiment analysis has become a hot research topic.Multiple sentiments of different targets when expressing multi-faceted in a sentence,it will cause problems such as feature overlap,which will have a negative impact on text sentiment classification.A capsule network-based model for aspect-level sentiment classification (SCACaps) was proposed.Sequential convolution was used to extract the features of context and aspect words separately,and an interactive attention mechanism was introduced to reduce the mutual influence on each other,and then the text feature representation was transmitted into the capsule network after reconstruction.The routing algorithm was optimized by introducing high-level capsule coefficients between the capsule layers,and the global parameters were shared in the entire iterative update process to save relatively complete text feature information.By comparing with multiple models,the SCACaps model has the best classification effect,and the SCACaps model also performs better in small sample learning.

Key words: capsule network, sequential convolution, interactive attention, aspect-level, sentiment classification

CLC Number:

TP301

Zhidong XU, Bingyang CHEN, Xiao WANG, et al. Research on capsule network-based for aspect-level sentiment classification[J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(3): 284-292.

Figures/Tables 11

胶囊网络	可调参数	参数值
初始胶囊	胶囊维度	8
	层数	1
	参数共享	是
高层胶囊	胶囊维度	16
	胶囊个数	2
	迭代次数	3
	层数	1
	参数共享	是
	激活函数	$\frac{{‖ s_{j} ‖}^{2} + ε}{0.5 + {‖ s_{j} ‖}^{2}}$

References 22

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数据集	积极情感		中性情感		消极情感
数据集	训练集/个	测试集/个	训练集/个	测试集/个	训练集/个	测试集/个
Restaurant	2 164	728	637	196	807	196
Laptop	994	341	464	169	870	128

参数	参数值
词向量维度	300
因果空洞卷积层数	5
卷积核大小	5
空洞因子	2n
残差模块数	4
激活函数	ReLU
优化函数	Adam
dropout	0.2

模型	Restaurant	Laptop
BiLSTM	77.13%	68.76%
Seq-CNN	77.19%	68.78%
ATAE-LSTM	78.48%	69.87%
IAN	78.61%	72.18%
MGAN	81.24%	75.38%
Bi-IAN	79.38%	72.24%
SCACaps	81.64%	76.26%

模型	Restaurant	Laptop
BiLSTM	63.74%	62.42%
Seq-CNN	63.79%	62.48%
ATAE-LSTM	63.38%	62.37%
IAN	68.13%	65.82%
MGAN	71.87%	72.38%
Bi-IAN	69.94%	66.74%
SCACaps	72.34%	72.43%

模型	accuracy	F1分数
BiLSTM	63.76%	—
Seq-CNN	64.03%	—
ATAE-LSTM	65.42%	—
IAN	65.57%	—
MGAN	71.84%	65.27%
Bi-IAN	66.32%	62.69%
SCACaps	73.28%	67.14%

Research on capsule network-based for aspect-level sentiment classification

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