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Data Analysis and Knowledge Discovery  2024, Vol. 8 Issue (1): 55-68    DOI: 10.11925/infotech.2096-3467.2022.1259
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Sentiment Analysis with Abstract Meaning Representation and Dependency Grammar
Li Xuelian1,Wang Bi2(),Li Lixin3,Han Dixuan4
1School of Foreign Studies, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
3School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
4School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta 30318, United States
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Abstract  

[Objective] This paper aims to combine the deep semantic representation and surface syntactic structure of natural language sentences. [Methods] We proposed an integration strategy based on semantic and syntactic rule concatenation and utilized it for the aspect-based sentiment analysis. This strategy used the answer set programming language (ASP) to represent abstract meaning representation (AMR), dependency grammar (DEP), and part of speech (POS) as ASP facts. It also integrated the DEP and POS through rule body extension based on AMR rules. Therefore, a sentence’s two or more language features were concatenated into the rule body. Based on this strategy, we developed the AMR-DEP-POS-C and AMR-DEP-C models. [Results] We examined the new methods on eight publicly available review datasets. The AMR-DEP-POS-C achieved a complementary relationship between semantics and syntax and performed better than the baseline methods based on semantic, syntactic, and deep learning. [Limitations] Our new models rely on the accuracy of the existing AMR and DEP parsers. [Conclusions] AMR-DEP-POS-C can effectively integrate different language features and bring new research perspectives and tools for aspect-based sentiment analysis.
Key words： Abstract Meaning Representation      Dependency Grammar      Rule      Aspect-Based Sentiment Analysis     
Received: 27 November 2022      Published: 28 April 2023
ZTFLH:  TP391  
  H03  
Fund:Innovation and Entrepreneurship of Jiangsu Province(JSSCBS20220624);Talent Project of Nanjing University of Posts and Telecommunications(XK0094522034);Jiangxi Provincial Natural Science Foundation(20232BAB212022)
Corresponding Authors: Wang Bi，ORCID：0000-0002-4365-0148，E-mail：wangbi@jxust.edu.cn。   
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Xuelian Li
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Cite this article:

Li Xuelian, Wang Bi, Li Lixin, Han Dixuan. Sentiment Analysis with Abstract Meaning Representation and Dependency Grammar. Data Analysis and Knowledge Discovery, 2024, 8(1): 55-68.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.1259     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2024/V8/I1/55

Seed Rule Construction Based on Rule Concatenation
类型 ASP事实
词性事实Factpos pos （the， dt）.
pos （camera， nn）.
pos （has， vbz）.
pos （a， dt）.
pos （good， jj）.
pos （battery，nn）.
语法事实Factdep dep（camera， det， the）.
dep（has，nsubj， camera）.
dep（has， dobj， battery）.
dep（battery，det， a）.
dep（battery， amod，good）.
语义事实Factamr amr（battery， mod，good）.
amr（battery，domain， camera）.
观点词事实Factopn opinionword（good）.
The ASP Facts of Sentence 1
编号 名称 句子数量 标注的评价对象数量
D1 Digital Camera 597 237
D2 Digital Camera 346 174
D3 Cell Phone 546 302
D4 MP3 Player 1 716 674
D5 DVD Player 740 296
D6 Restaurant14（Train） 3 044 3 699
Restaurant14（Test） 800 1 134
D7 Laptop14（Train） 3 048 2 373
Laptop14（Test） 800 654
D8 Restaurant15（Train） 1 315 1 279
Restaurant15（Test） 685 597
Detailed Information of the Datasets of Opinion Target Extraction
实验环境 类别 参数
硬件环境 CPU Intel（R） Core（TM） i53210M CPU @2.50GHz
内存 14GB
软件环境 系统 Ubuntu 16.04.4 LTS
编程语言 Python3.6.5 |Anconda
Python 编辑器 VIM Vi IMproved 7.4 & Jupyter Notebook
回答集求解器版本 clingo-4.5.4
Detailed Information of Experimental Environment
方法 评价指标 D1 D2 D3 D4 D5 平均 D6 D7 D8 平均
DP mul 70.7 73.6 76.5 69.7 63.0 70.7 84.3 70.7 78.2 77.8
dis 60.0 59.6 58.1 54.0 52.8 56.9 73.2 67.7 69.4 70.1
DP+ mul 66.2 65.7 65.6 62.2 58.8 63.7 75.2 63.1 69.8 69.4
dis 46.6 46.3 45.9 46.1 45.6 46.1 62.4 59.2 59.1 60.3
RSDP mul 86.2 88.6 84.2 81.0 84.1 84.8 86.9 83.0 86.6 85.5
dis 82.7 82.8 76.1 72.0 74.5 77.6 78.5 79.7 81.9 80.1
RSDP+ mul 85.2 87.0 83.3 80.8 85.5 84.4 88.7 83.1 87.5 86.4
dis 80.9 83.1 76.3 68.5 78.3 77.4 82.0 80.1 83.2 81.8
DEP mul 75.5 86.2 75.0 72.1 71.8 76.1 88.4 65.3 74.2 76.0
dis 67.0 78.3 64.0 59.1 60.6 65.8 79.3 61.6 64.4 68.4
AMR mul 69.2 79.3 76.0 68.3 71.7 72.9 72.2 67.5 71.6 70.4
dis 60.6 68.5 65.7 56.4 68.9 64.0 59.4 65.8 56.1 60.4
CRF mul 79.3 84.0 80.0 87.0 74.4 80.9 - - - -
dis 63.8 73.5 70.6 80.2 65.6 70.7 78.3 62.1 - -
AMR-POS mul 76.0 84.2 76.6 75.3 79.1 78.2 83.8 64.4 85.2 77.8
dis 70.3 75.2 67.8 67.3 71.5 70.4 72.7 62.3 79.3 71.4
AMR-DEP-C mul 80.5 88.5 87.8 71.9 69.4 79.6 81.9 81.7 76.9 80.2
dis 75.3 81.0 80.5 57.9 64.1 71.7 68.8 75.9 63.9 69.5
AMR-DEP-POS-C mul 83.8 89.0 85.8 81.7 87.2 85.5 90.2 87.6 91.5 89.8
dis 77.3 83.2 79.2 72.0 83.9 79.1 82.7 82.9 86.5 84.0
Precision of the Baselines and Our Methods on D1-D8
方法 评价指标 D1 D2 D3 D4 D5 平均 D6 D7 D8 平均
DP mul 91.0 89.5 90.2 88.7 89.6 89.8 86.0 75.4 89.9 83.8
dis 83.9 78.8 81.4 74.7 76.3 79.0 74.3 69.4 83.1 75.6
DP+ mul 96.3 95.9 95.1 95.6 94.3 95.4 92.6 89.5 95.2 92.4
dis 91.4 89.4 87.6 88.0 87.1 88.7 86.4 84.2 92.0 87.5
RSDP mul 88.1 87.8 82.9 81.5 78.9 83.8 82.7 69.2 83.2 78.4
dis 74.2 75.8 67.0 66.0 58.1 68.2 68.9 61.7 73.1 67.9
RSDP+ mul 90.5 93.6 89.5 90.4 90.0 90.8 84.9 69.8 84.9 79.9
dis 75.3 83.3 73.2 78.0 67.7 75.5 71.6 62.5 75.9 70.0
DEP mul 87.7 93.7 82.8 88.7 84.0 87.4 78.7 79.4 78.4 78.8
dis 78.5 83.6 71.4 74.7 61.7 74.0 60.6 72.1 65.8 66.2
AMR mul 63.5 80.5 76.6 87.1 75.8 76.7 81.5 58.0 85.4 75.0
dis 53.8 64.2 64.3 71.3 46.8 60.1 65.6 51.0 71.8 62.8
CRF mul 67.9 74.4 51.1 70.3 69.9 66.7 - - - -
dis 52.7 54.6 45.4 50.0 50.5 50.6 71.6 57.8 - -
AMR-POS mul 95.5 94.8 93.1 90.8 93.2 93.5 85.0 73.6 75.8 78.1
dis 90.3 88.1 82.7 81.3 86.2 85.8 71.8 68.1 59.8 66.6
AMR-DEP-C mul 90.2 92.5 90.3 90.8 85.4 89.9 87.9 73.3 87.8 83.0
dis 78.5 80.6 75.5 77.3 63.8 75.2 76.4 65.8 77.5 73.2
AMR-DEP-POS-C mul 93.4 96.6 87.2 89.2 90.4 91.4 87.7 72.1 86.7 82.2
dis 86.0 91.0 75.5 78.0 79.8 82.1 76.4 64.8 75.9 72.4
Recall of the Baselines and Our Methods on D1-D8
方法 评价指标 D1 D2 D3 D4 D5 平均 D6 D7 D8 平均
DP mul 80.0 80.8 82.8 78.1 74.0 79.1 85.2 73.0 83.6 80.6
dis 70.0 67.9 67.9 62.6 62.4 66.1 73.7 68.5 75.6 72.6
DP+ mul 78.5 78.0 77.6 75.4 72.4 76.4 83.0 74.0 80.5 79.2
dis 61.7 61.0 60.3 60.5 59.8 60.7 72.5 69.5 72.0 71.3
RSDP mul 87.1 88.2 83.5 81.2 81.4 84.3 84.8 75.5 84.9 81.7
dis 78.2 79.1 71.3 68.8 65.3 72.5 73.4 69.6 77.3 73.4
RSDP+ mul 87.8 90.2 86.3 85.3 87.7 87.4 86.7 75.9 86.2 82.9
dis 78.0 83.2 74.7 73.0 72.7 76.3 76.4 70.2 79.4 75.3
DEP mul 81.2 89.8 78.7 79.6 77.4 81.3 83.3 71.7 76.2 77.1
dis 72.3 80.8 67.5 66.0 61.2 69.6 68.7 66.5 65.1 66.8
AMR mul 66.2 79.9 76.3 76.6 73.7 74.5 76.5 62.4 77.9 72.3
dis 57.0 66.3 65.0 63.0 55.7 61.4 62.4 57.5 63.0 61.0
CRF mul 73.1 78.9 62.3 77.8 72.1 72.9 - - - -
dis 57.7 62.6 55.2 61.6 57.1 58.9 74.8 59.9 - -
AMR-POS mul 84.7 89.2 84.0 82.3 85.6 85.2 84.4 68.7 80.2 77.8
dis 79.0 81.1 74.5 73.7 78.2 77.3 72.3 65.1 68.2 68.5
AMR-DEP-C mul 85.1 90.5 89.0 80.2 76.6 84.3 84.8 77.2 82.0 81.3
dis 76.9 80.8 77.9 66.2 64.0 73.1 72.4 70.4 70.0 70.9
AMR-DEP-POS-C mul 88.4 92.6 86.5 85.3 88.8 88.3 88.9 79.1 89.0 85.7
dis 81.4 86.9 77.3 74.9 81.8 80.5 79.4 72.8 80.8 77.7
F-Score of the Baselines and Our Methods on D1-D8
Performance of AMR-DEP-POS-C，R-DNN and CRF
方法 最终规则
数量		 准确率
（%）		 召回率
（%）		 F1值
（%）
RSDP+ 130 82.0 71.6 76.4
AMR-POS 227 72.7 71.8 72.3
AMR-DEP-C 360 68.8 76.4 72.4
AMR-DEP-POS-C 990 82.7 76.4 79.4
Rule Numbers and Performance of Four Methods
方法 实例化
规则数量		 候选规则
数量		 最终规则
数量		 运行时间
（min）
RSDP+ 313 215 130 44
AMR-POS 613 329 227 61
AMR-DEP-C 1 528 559 360 113
AMR-DEP-POS-C 3 725 1 976 990 259
Running Time and Rule Number of Four Methods
编号 RSDP+ AMR-POS AMR-DEP-POS-C 评价对象数量
1 - - - 95
2 - - + 13
3 - + - 31
4 - + + 18
5 + - - 15
6 + - + 33
7 + + - 9
8 + + + 309
The Extracted Aspects of Three Methods
编号 错误类型 具体类别 错误评价
对象占比
1 第一类
错误		 词性标注错误 25%
2 依存语法解析错误
3 AMR解析错误
4 第二类
错误		 评价对象与观点词无一阶语法关系 63%
5 评价对象与观点词无二阶语法关系
6 评价对象之间无一阶语法关系
7 评价对象之间无二阶语法关系
8 评价对象与观点词无一阶语义关系
9 评价对象与观点词无二阶语义关系
10 评价对象之间无一阶语义关系
11 评价对象之间无二阶语义关系
12 第三类错误 第三类错误约束条件多 12%
Error Type of Aspect Extracted by AMR-DEP-POS-C
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