Aspect-based sentiment analysis with syntactic prompt

doi:10.11772/j.issn.1001-9081.2024050594

Journal of Computer Applications ›› 0, Vol. ›› Issue (): 35-43.DOI: 10.11772/j.issn.1001-9081.2024050594

• Artificial intelligence • Previous Articles Next Articles

Aspect-based sentiment analysis with syntactic prompt

Wei ZHANG¹, Zhuyu CHU¹, Xueqi CHEN¹, Xuehui FU¹, Chenchen WANG²(), Chen LI², Haiyan WU²

^1.Zhejiang Zheneng Linhai Offshore Wind Power Company Limited，Taizhou Zhejiang 317000，China
^2.School of Information Management and Artificial Intelligence，Zhejiang University of Finance and Economics，Hangzhou Zhejiang 310018，China

Received:2024-05-06 Revised:2024-07-10 Accepted:2024-07-11 Online:2025-01-24 Published:2024-12-31
Contact: Chenchen WANG

基于句法提示的细粒度情感分析

章巍¹, 储著宇¹, 陈学奇¹, 傅学辉¹, 王晨晨²(), 李晨², 吴海燕²

^1.浙江浙能临海海上风力发电有限公司，浙江台州 317000
^2.浙江财经大学信息管理与人工智能学院，杭州 310018

通讯作者: 王晨晨
作者简介:章巍（1973—），男，浙江宁波人，高级工程师，硕士，主要研究方向：发电系统规划与建设、电力系统与人工智能
储著宇（1981—），男，安徽潜山人，高级工程师，硕士，主要研究方向：发电厂继电保护、调度数据网自动化
陈学奇（1980—），男，浙江临海人，工程师，主要研究方向：发电厂智能检测与智能控制
傅学辉（1985—），男，浙江台州人，工程师，主要研究方向：发电厂继电保护、调度数据网自动化
王晨晨（2003—），男，浙江杭州人，主要研究方向：自然语言处理、情感计算
李晨（1999—），男，内蒙古乌海人，硕士，主要研究方向：情感计算
吴海燕（1985—），女，陕西延安人，讲师，博士，CCF会员，主要研究方向：自然语言处理。
基金资助:
国家自然科学基金资助项目(62306267);浙江省自然科学基金资助项目(LY22F020027)

Abstract

Abstract:

The core idea of prompt tuning is to insert a prompt template into the original input text and convert the classification problem into a language model containing mask code to predict the occurrence probability of the mask code in a sentence. In Aspect-Based Sentiment Analysis （ABSA） tasks， determining appropriate prompt templates requires corresponding syntactic knowledge， and constructing effective sentiment labels is very time-consuming. Furthermore， the semantic information and priori knowledge contained in complex implicit sentimental opinions cannot be ignored. Therefore， a syntactic prompt template was introduced， which incorporated syntactic knowledge （such as phrase structure and dependency relations） into the prompt templates for sentimental opinion mining related to sentiment aspect words to enhance the capture of explicit or implicit sentiment relation pairs. Experimental results on four publicly available datasets show that the proposed syntactic knowledge fused prompt model， SynPrompt （Syntax aware Prompt-tuning）， is effective； and compared to dotGCN （discrete opinion tree Graph Convolutional Network），DualGCN （Dual Graph Convolutional Network）， and dotGCN models， it has improvements of 0.81%， 0.27%， and 0.09% in accuracy， respectively. In addition， results of ablation experiments and case studies demonstrate that syntactic knowledge is effective on both explicit and implicit sentimental prompts. With 8-shot， by means of data augmentation， the F1 scores of SynPrompt model are improved by 31.62%， 42.02%， 121.04%， and 35.01% on the four datasets， respectively. However， with 16 and 32-shot， the accuracies of SynPrompt model are not improved significantly， indicating that the data augmentation approach is effective on few-shot datasets and enhances the ability of SynPrompt model to capture information.

Key words: syntactic information, prompt fine-tuning, few-shot learning, knowledge base enhancement, sentiment analysis

摘要：

提示微调的核心思想是在原输入文本中插入提示模板，并将分类问题转换为含有遮掩码的语言模型预测遮掩码在句子中出现的概率。细粒度情感分析（ABSA）任务中，确定一个合适的提示模板需要相应的句法知识，而构建有效的情感标签非常耗时，而且不能忽略复杂的隐式情感观点中蕴含的语义信息和先验知识。因此，介绍一个句法提示模板，该模板将句法知识（如短语结构、依存关系）融合到情感方面词相关的情感观点挖掘的提示模板中，以增强显式或隐式情感关系对的捕获。在4个公开数据集上的实验结果表明，所提融合句法知识的提示模型SynPrompt （Syntax aware Prompt-tuning）是有效的，它在Restaurant、Laptop和MAMS数据集上分别与dotGCN （Discrete Opinion Tree Graph Convolutional Network）、DualGCN （Dual Graph Convolutional Network）和dotGCN模型相比准确率分别提升了0.81%、0.27%和0.09%。此外，消融实验和案例分析的结果表明了句法知识在显式和隐式情感提示上都是有效的。在8-Shot时，通过数据增强的方式使SynPrompt模型在4个数据集上的F1分数分别提升了31.62%、42.02%、121.04%和35.01%；然而在16、32-shot时，SynPrompt模型的准确率并没有显著提升。这说明数据增强的方式在小样本数据集上是有效的，并增强了句法提示SynPrompt模型捕获信息的能力。

关键词: 句法信息, 提示微调, 小样本学习, 知识库增强, 情感分析

CLC Number:

TP391.1

Wei ZHANG, Zhuyu CHU, Xueqi CHEN, Xuehui FU, Chenchen WANG, Chen LI, Haiyan WU. Aspect-based sentiment analysis with syntactic prompt[J]. Journal of Computer Applications, 0, (): 35-43.

章巍, 储著宇, 陈学奇, 傅学辉, 王晨晨, 李晨, 吴海燕. 基于句法提示的细粒度情感分析[J]. 《计算机应用》唯一官方网站, 0, (): 35-43.

Figures/Tables 17

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词	短语路径	词	短语路径
Apple	S→S→NP→NNP	the	S→S→NP→DT
has	S→S→VP→VBZ	price	S→S→NP→NN
higher	S→S→VP→NP→JJR	…	…
resolution	S→S→VP→NP→NN	expensive	S→S→VP→ADJP→JJ
but	S→CC

词	短语路径	词	短语路径
Apple	S→S→NP→NNP	the	S→S→NP→DT
has	S→S→VP→VBZ	price	S→S→NP→NN
higher	S→S→VP→NP→JJR	…	…
resolution	S→S→VP→NP→NN	expensive	S→S→VP→ADJP→JJ
but	S→CC

词	依存路径	词	依存路径
Apple	ROOT→nsubj	the	ROOT→obj→nsubj→det
has	ROOT	price	ROOT→obj→nsubj
higher	ROOT→obj→amod	…	…
resolution	ROOT→obj	expensive	ROOT→obj
but	ROOT→obj→cc

词	依存路径	词	依存路径
Apple	ROOT→nsubj	the	ROOT→obj→nsubj→det
has	ROOT	price	ROOT→obj→nsubj
higher	ROOT→obj→amod	…	…
resolution	ROOT→obj	expensive	ROOT→obj
but	ROOT→obj→cc

数据集	正向（pos）		中性（neu）		负向（neg）
数据集	训练集	测试集	训练集	测试集	训练集	测试集
Restaurant	2 164	727	637	196	807	196
Laptop	976	337	455	167	851	128
Twitter	1 507	172	3 016	336	1 528	169
MAMS	3 380	400	5 042	607	2 764	329

Aspect-based sentiment analysis with syntactic prompt

基于句法提示的细粒度情感分析

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Figures/Tables 17

References 44

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Metrics

模型	Restaurant		Laptop		Twitter		MAMS
模型	Accuracy	F1	Accuracy	F1	Accuracy	F1	Accuracy	F1
ATAE-LSTM （2015）	77.20	—	68.70	—	—	—	—	—
TD-LSTM （2016）	79.10	69.00	71.22	65.75	69.51	67.98	—	—
RAM （2017）	80.23	70.80	74.49	71.35	69.36	67.30	—	—
BERT （2019）	83.62	78.28	77.58	72.38	75.28	74.11	82.82	81.90
PhraseRNN （2015）	66.20	59.32	—	—	—	—	—	—
SynATT （2018）	80.45	71.26	77.57	69.13	—	—	—	—
CDT （2019）	82.30	74.02	77.19	72.99	74.66	73.66	80.70	79.79
ASGCN （2019）	80.77	72.02	75.55	71.05	72.15	70.40	—	—
R-GAT （2020）	86.60	81.35	78.21	74.07	76.15	74.88	81.75	80.87
DGEDT （2020）	86.30	80.00	79.80	75.60	77.90	75.40	—	—
TD-GAT （2020）	80.35	76.13	74.13	72.01	72.68	71.15	—	—
SD-GAT （2020）	83.57	76.47	81.35	78.34	—	—	—	—
DualGCN （2021）	86.77	81.62	80.63	77.36	76.04	74.91	—	—
Dual-MRC （2021）	—	82.04	—	75.97	—	—	—	—
DR-BERT （2022）	—	—	—	—	77.24	76.10	—	—
dotGCN （2022）	86.16	80.49	81.03	78.10	78.11	77.00	84.95	84.44
SynPrompt	86.86	80.91	80.85	78.55	76.78	75.98	85.03	84.48

类别	模型	Restaurant		Laptop		MAMS
类别	模型	Accuracy	F1	Accuracy	F1	Accuracy	F1
w/ dis_n.	SynPrompt	86.86	80.91	80.85	78.55	85.03	84.48
	w/o con.	86.51	79.76	79.59	76.65	84.66	84.25
	w/o dep.	86.42	79.23	78.64	74.99	85.10	84.73
	w/o con. & dep.	86.24	80.05	79.27	75.39	84.51	83.92
w/o dis_n.	SynPrompt	86.42	80.58	78.80	75.35	84.88	84.27
w/o dis_n.	Prompt	85.08	77.77	78.48	73.93	84.36	83.80

序号	评论句子（显式情感）	方面词	w/o con. & dep.	w/o dep.	w/o con.	SynPrompt
1	The sushi_pos is always fresh and yummy and the menu_pos is pretty varied.	sushi	pos√	pos√	pos√	pos√
1		menu	neu×	pos√	pos√	pos√
2	I would only go for the coffee_pos which is way better than Starbucks or the like.	coffee	neu×	pos√	pos√	pos√
3	If your looking for nasty high priced_negfood_neg with ghetto scenery it's here.	priced	pos×	pos×	neg√	neg√
3		food	neg√	neg√	neg√	neg√
4	I reccomend the fried pork dumplings_pos，orange chicken_pos， and fried rice_pos .	dumplings	pos√	pos√	pos√	pos√
		chicken	pos√	pos√	pos√	pos√
		rice	neu×	pos√	pos√	pos√

序号	评论句子（隐式情感）	方面词	w/o con. & dep.	w/o dep.	w/o con.	SynPrompt
1	For priced_neg the you pay for the food_neg， it's on par with other Japanese restaurants.	price	neu×	neg√	neg√	neg√
1		food	neu×	neg√	neg√	neg√
2	The sushi_pos is cut in blocks bigger than my cell phone.	sushi	neu×	neg√	neg√	neg√
3	It doesn't look like much on the outside， but the minute you walk inside， it's a whole other atmosphere_pos.	atmosphere	neu×	pos√	neu×	pos√

词	POS（词性）	词	POS（词性）
Hence	RB （副词）	is	VBZ （系动词）
the	DT （定冠词）	pretty	RB（副词）
food	NN （名词）	good	JJ（形容词）
itself	PRP （人称代词）

k-shot	模型	Restaurant	Laptop	Twitter	MAMS
8-shot	SynPrompt	38.65	40.27	18.39	38.42
	SynPrompt+	50.87	57.19	40.65	51.87
	w/o con.	39.25	41.34	18.29	37.32
	w/o dep.	39.38	41.07	18.08	38.25
16-shot	SynPrompt	59.89	62.52	43.64	53.91
	SynPrompt+	54.92	63.16	42.10	55.92
	w/o con.	60.70	62.73	44.23	54.04
	w/o dep.	60.32	63.40	44.08	53.97
32-shot	SynPrompt	64.84	66.57	50.54	57.55
	SynPrompt+	66.46	66.81	49.22	55.54
	w/o con.	64.09	67.66	50.39	56.41
	w/o dep.	64.41	66.67	50.78	57.03

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