Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (6A): 240400193-7.doi: 10.11896/jsjkx.240400193

• Artificial Intelligence • Previous Articles     Next Articles

Aspect-level Sentiment Analysis Models Based on Syntax and Semantics

HUANG Zhiyong, LI Bicheng, WEI Wei   

  1. College of Computer Science and Technology,Huaqiao University,Xiamen,Fujian 361000,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:HUANG Zhiyong,born in 1994,postgraduate.His main research interests include natural language processing,emotional analysis and topic detection.
    LI Bicheng,born in 1970,Ph.D,professor,Ph.D supervisor.His main research interests include intelligent information processing,network ideological security,network public opinion monitoring and guidance,and big data analysis and mining.
  • Supported by:
    Joint Fund of Ministry of Education for Equipment Pre-research(8091B022150).

PDF (PC)

68

Abstract: As more and more people express their opinions online,the prevalence of emotionally charged posts is gradually increasing.The accumulation of negative emotions may lead to the loss of control over public opinion.Accurately identifying the emotional polarity of posts can effectively analyze the current state of public opinion.Current aspect-level sentiment analysis has not effectively integrated syntactic and semantic information,failing to simultaneously consider the complementarity of grammatical structures and semantic relevance.Therefore,a model for aspect-level sentiment analysis that integrates syntax and semantics(SS-GCN) is proposed,comprising syntax analysis module,semantics analysis module,and fusion module.Firstly,the text is input to a pre-trained BERT model to obtain feature representations of syntactic relationships through the syntax analysis module.Simultaneously,the semantics analysis module,enhanced by a neighborhood enhancement mechanism,captures feature representations of semantic relevance.Finally,both representations are input to the fusion module,where under the action of affine transformation,syntactic and semantic information are effectively interacted and integrated,achieving aspect-level sentiment analysis.

Key words: Sentiment analysis, Sentiment classification, Fine-grained, Semantic, Syntax, Fusion

CLC Number: 

  • TP391
[1]SONG W,WEN Z,XIAO Z,et al.Semantics perception and refinement network for aspect-based sentiment analysis[J].Knowledge-Based Systems,2021,214:106755.
[2]CHENG L C,CHEN Y L,LIAOY Y.Aspect-based sentimentanalysis with component focusing multi-head co-attention networks[J].Neurocomputing,2022,489:9-17.
[3]HUANG Y,PENG H,LIU Q,et al.Attention-enabled gatedspiking neural P model for aspect-level sentiment classification[J].Neural Networks,2023,157:437-443.
[4]GU T,ZHAO H,HE Z,et al.Integrating external knowledgeinto aspect-based sentiment analysis using graph neural network[J].Knowledge-Based Systems,2023,259:110025.
[5]PHAN M H,OGUNBONA P O.Modelling context and syntactical features for aspect-based sentiment analysis[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.2020:3211-3220.
[6]ZHANG M,QIAN T.Convolution over hierarchical syntacticand lexical graphs for aspect level sentiment analysis[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing(EMNLP).2020:3540-3549.
[7]XIAO L,HU X,CHEN Y,et al.Targeted sentiment classification based on attentional encoding and graph convolutional networks[J].Applied Sciences,2020,10(3):957.
[8]LIU N,SHEN B.Aspect-based sentiment analysis with gated alternate neural network[J].Knowledge-Based Systems,2020,188:105010.
[9]ZHAO M,YANG J,ZHANG J,et al.Aggregated graph convolutional networks for aspect-based sentiment classification[J].Information Sciences,2022,600:73-93.
[10]LIANG S,WEI W,MAO X L,et al.BiSyn-GAT+:Bi-syntaxaware graph attention network for aspect-based sentiment analysis[J].arXiv:2204.03117,2022.
[11]ZHANG C,LI Q,SONG D.Aspect-based sentiment classification with aspect-specific graph convolutional networks[J].ar-Xiv:1909.03477,2019.
[12]SUN K,ZHANG R,MENSAH S,et al.Aspect-level sentiment analysis via convolution over dependency tree[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing(EMNLP-IJCNLP).2019:5679-5688.
[13]CHEN C,TENG Z,ZHANG Y.Inducing target-specific latentstructures for aspect sentiment classification[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing(EMNLP).2020:5596-5607.
[14]LIANG B,YIN R,GUI L,et al.Jointly learning aspect-focused and inter-aspect relations with graph convolutional networks for aspect sentiment analysis[C]//Proceedings of the 28th International Conference on Computational Linguistics.2020:150-161.
[15]WANG K,SHEN W,YANG Y,et al.Relational graph attention network for aspect-based sentiment analysis[J].arXiv:2004.12362,2020.
[16]LI R,CHEN H,FENG F,et al.Dual graph convolutional networks for aspect-based sentiment analysis[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing(Volume 1:Long Papers).2021:6319-6329.
[17]MA Y,PENG H,CAMBRIA E.Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM[C]//Proceedings of the AAAI conference on artificial intelligence.2018,32(1).
[18]ZHOU J,HUANG J X,HUQ V,et al.Sk-gcn:Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification[J].Knowledge-Based Systems,2020,205:106292.
[19]ZHONG Q,DING L,LIU J,et al.Knowledge graph augmented network towards multiview representation learning for aspect-based sentiment analysis[J].IEEE Transactions on Knowledge and Data Engineering,2023,35(10):10098-10111.
[20]WANG Y,HUANG M,ZHU X,et al.Attention-based LSTMfor aspect-level sentiment classification[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.2016:606-615.
[21]TANG D,QIN B,LIU T.Aspect level sentiment classificationwith deep memory network[J].arXiv:1605.08900,2016.
[22]MA D,LI S,ZHANG X,et al.Interactive attention networks for aspect-level sentiment classification[J].arXiv:1709.00893,2017.
[23]HUANG B,OU Y,CARLEY K M.Aspect level sentiment classification with attention-over-attention neural networks[C]//11th International Conference Social,Cultural,and Behavioral Modeling(SBP-BRiMS 2018).Washington,DC,USA,Springer International Publishing,2018:197-206.
[24]FAN F,FENG Y,ZHAO D.Multi-grained attention network for aspect-level sentiment classification[C]//Proceedings of the 2018 cCnference on Empirical Methods in Natural Language Processing.2018:3433-3442.
[25]ZHANG M,QIAN T.Convolution over hierarchical syntacticand lexical graphs for aspect level sentiment analysis[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing(EMNLP).2020:3540-3549.
[26]LU Q,ZHU Z,ZHANG G,et al.Aspect-gated graph convolutional networks for aspect-based sentiment analysis[J].Applied Intelligence,2021,51(7):4408-4419.
[27]ZHANG F,ZHANG Y,HOU S,et al.Aspect Fusion Graph Convolutional Networks for Aspect-Based Sentiment Analysis[C]//27th China Conference Information Retrieval(CCIR 2021).Dalian,China,Springer International Publishing,2021:74-87.
[28]XIA Y,WU W N,YANG C C.Graph Convolutional Network-Based Multi-Interaction Attention Aspect-Level Sentiment Analysis[J].Computer Engineering and Design,2023,44(12):3691-3699.
[1] WANG Rong , ZOU Shuping, HAO Pengfei, GUO Jiawei, SHU Peng. Sand Dust Image Enhancement Method Based on Multi-cascaded Attention Interaction [J]. Computer Science, 2025, 52(6A): 240800048-7.
[2] JIN Lu, LIU Mingkun, ZHANG Chunhong, CHEN Kefei, LUO Yaqiong, LI Bo. Pedestrian Re-identification Based on Spatial Transformation and Multi-scale Feature Fusion [J]. Computer Science, 2025, 52(6A): 240800156-7.
[3] LUAN Fangjun, ZHANG Fengqiang, YUAN Shuai. Click-through Rate Prediction Model Based on Feature Embedding Gating and PolynomialFeature Crossover Networks [J]. Computer Science, 2025, 52(6A): 240900092-6.
[4] ZHANG Yongyu, GUO Chenjuan, WEI Hanyue. Deep Learning Stock Price Probability Prediction Based on Multi-modal Feature Wavelet Decomposition [J]. Computer Science, 2025, 52(6A): 240600140-11.
[5] FANG Rui, CUI Liangzhong, FANG Yuanjing. Equipment Event Extraction Method Based on Semantic Enhancement [J]. Computer Science, 2025, 52(6A): 240900096-9.
[6] WANG Jiamin, WU Wenhong, NIU Hengmao, SHI Bao, WU Nier, HAO Xu, ZHANG Chao, FU Rongsheng. Review of Concrete Defect Detection Methods Based on Deep Learning [J]. Computer Science, 2025, 52(6A): 240900137-12.
[7] XU Yutao, TANG Shouguo. Visual Question Answering Integrating Visual Common Sense Features and Gated Counting Module [J]. Computer Science, 2025, 52(6A): 240800086-7.
[8] LIU Bingzhi, CAO Yin, ZHOU Yi. Distillation Method for Text-to-Audio Generation Based on Balanced SNR-aware [J]. Computer Science, 2025, 52(6A): 240900125-5.
[9] HOU Zhexiao, LI Bicheng, CAI Bingyan, XU Yifei. High Quality Image Generation Method Based on Improved Diffusion Model [J]. Computer Science, 2025, 52(6A): 240500094-9.
[10] WANG Rui, TANG Zhanjun. Multi-feature Fusion and Ensemble Learning-based Wind Turbine Blade Defect Detection Method [J]. Computer Science, 2025, 52(6A): 240900138-8.
[11] LI Mingjie, HU Yi, YI Zhengming. Flame Image Enhancement with Few Samples Based on Style Weight Modulation Technique [J]. Computer Science, 2025, 52(6A): 240500129-7.
[12] ZOU Rui, YANG Jian, ZHANG Kai. Low-resource Vietnamese Speech Synthesis Based on Phoneme Large Language Model andDiffusion Model [J]. Computer Science, 2025, 52(6A): 240700138-6.
[13] YIN Baosheng, ZONG Chen. Research on Semantic Fusion of Chinese Polysemous Words Based on Large LanguageModel [J]. Computer Science, 2025, 52(6A): 240400139-7.
[14] WANG Yicheng, NING Tai, LIU Xinyu, LUO Ye. Position-aware Based Multi-modality Lung Cancer Survival Prediction Method [J]. Computer Science, 2025, 52(6A): 240500089-8.
[15] SHI Xincheng, WANG Baohui, YU Litao, DU Hui. Study on Segmentation Algorithm of Lower Limb Bone Anatomical Structure Based on 3D CTImages [J]. Computer Science, 2025, 52(6A): 240500119-9.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.