基于辅助分类网络的跨领域文本情感分类

doi:10.16182/j.issn1004731x.joss.21-1283

摘要/Abstract

摘要：

为了使源域与目标域中同类情感文本准确对齐，且尽可能增大不同情感文本特征差异，提出了一种具有加权对抗网络的域适应模型。提出了一种主分类网络与辅助分类网络相结合的网络结构，主分类网络用于对源域文本进行有监督学习，辅助分类网络用来提高文本特征的可区分度；提出了一种多对抗网络权重计算方法，实现域间同类样本的准确对齐。实验结果表明：对于Amazon数据集，提出的模型对于目标域中文本的平均识别准确率可达84.22%，比对比模型提升了2.07%，说明该模型可将优化得到的特征提取器与特征分类器同时较好的适用于源域与目标域中，从而对不同领域文本分析仿真建模提供了可靠的数据。

关键词: 文本情感分类, 域适应, 对抗网络, 辅助分类网络

Abstract:

To align exactly the texts with same sentiment polarities of source and target domains, and to enlarge the feature difference of different sentiment texts as much as possible, a domain adaptation model with weighted adversarial networks is proposed. A new structured classification network consisting of a main classification network and an auxiliary classification network is proposed, in which the main classification network is used to perform supervised learning on the labeled texts of the source domain, and the auxiliary classification network is used to improve the distinguishability of the text features. A calculation method of multiple adversarial network weights is proposed to realize the exact alignment of same class samples of different domains. Experimental results show that, for Amazon dataset, the average recognition accuracy for the texts of target domains can reach 84.22%, which is 2.07% higher than the compared models. The optimized feature extractor and the feature classifier can be applied to the source and target domains simultaneously on the proposed model, and can provide reliable data for the simulation and modeling of text analysis in different fields.

Key words: text sentiment classification, domain adaptation, adversarial network, auxiliary classification network

中图分类号:

TP391.4

马娜, 温廷新, 贾旭, 李晓会. 基于辅助分类网络的跨领域文本情感分类[J]. 系统仿真学报, 2023, 35(4): 721-733.

Na Ma, Tingxin Wen, Xu Jia, Xiaohui Li. Cross-domain Text Sentiment Classification Based on Auxiliary Classification Networks[J]. Journal of System Simulation, 2023, 35(4): 721-733.

图/表 14

图1

图2

图3

表1

表2

不同参数下的识别结果

$λ d$	$λ c$
$λ d$	0.1	0.5	1.0	5.0	10.0
0.1	0.774	0.795	0.821	0.814	0.769
0.5	0.814	0.826	0.832	0.792	0.788
1.0	0.768	0.796	0.812	0.785	0.772
5.0	0.744	0.785	0.776	0.765	0.748
10.0	0.759	0.779	0.751	0.771	0.749

表2

图4

图5

图6

图7

图8

图9

表3

不同算法在目标域识别结果 (%)

源域→目标域	SVM	AuxNN	DANN	PBLM	DAS	ACAN	本文
平均值	73.66	79.85	80.29	80.40	81.96	82.15	84.22
$D → B$	75.20	80.80	81.70	82.50	82.05	82.35	82.41
$E → B$	68.85	78.00	78.55	71.40	80.00	79.75	82.57
$K → B$	70.00	77.85	79.25	74.20	80.05	80.80	82.54
$B → D$	77.15	81.75	82.30	84.20	82.75	83.45	85.36
$E → D$	69.50	80.65	79.70	75.00	80.15	81.75	82.82
$K → D$	71.40	78.90	80.45	79.80	81.40	82.10	83.32
$B → E$	72.15	76.40	77.60	77.60	81.15	81.30	81.26
$D → E$	71.65	77.55	79.70	79.60	81.55	82.80	84.88
$K → E$	79.75	84.05	86.65	87.10	85.80	86.60	89.13
$B → K$	73.50	78.10	76.10	82.50	82.25	83.05	84.72
$D → K$	72.00	80.05	77.35	83.20	81.50	78.60	82.89
$E → K$	82.80	84.15	83.95	87.80	84.85	83.35	88.76

表3

图10

表4

基于评论文本的用户-物品兴趣分析

物品	$u 1$	$u 2$
手机 $i 1$ 评论文本	打游戏不卡顿，画面流畅	手机续航能力强
书籍 $i 2$ 评论文本	章节安排不是很合理	存在错别字，图印刷不清晰
台式机 $i 3$ 评论文本	处理器与显卡性能好	系统运行非常稳定，从不死机
游戏笔记本 $i 4$ 兴趣度	高	？
商务笔记本 $i 5$ 兴趣度	低	？

表4

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域	样本数目	正样本示例	负样本示例
B	4 465	这是一本非常棒的书，老少皆宜，它以一种有趣的方式展示了为什么我们需要一种通用的测量形式	这本书传达了一个很好的信息，可能会对我产生持久的影响，但情节非常乏味，书的中间部分基因和鳍鱼的关系似乎与书的其余部分不相适应，不推荐
D	3 586	如果你没有这张DVD，你需要把它添加到你的收藏中。在我看来，这是有史以来最好的美国动画电影	对于那些对这部电影赞不绝口的人来说，你一定是在另一个维度或别的什么地方
E	5 681	我非常喜欢它，我把它用在野外狩猎的户外小径相机里，它能拍几张照片，在各种天气的相机里，我还会再买金斯顿	我最近刚买了这个，当我安装它时，诺顿在安装光盘上捕获了2个病毒
K	5 945	我很喜欢用这个铸造锅，它煮得很好吃，容量很大	绝对的劣质产品，不要浪费你的钱，当你磨任何东西的时候，它都不会固定住，这个产品肯定会让你发脾气

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