{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T15:08:45Z","timestamp":1784560125398,"version":"3.55.0"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T00:00:00Z","timestamp":1691971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in natural language processing (NLP) that aims to extract triplets from comments. Each triplet comprises an aspect term, an opinion term, and the sentiment polarity of the aspect term. The neural network model developed for this task can enable robots to effectively identify and extract the most meaningful and relevant information from comment sentences, ultimately leading to better products and services for consumers. Most existing end-to-end models focus solely on learning the interactions between the three elements in a triplet and contextual words, ignoring the rich affective knowledge information contained in each word and paying insufficient attention to the relationships between multiple triplets in the same sentence. To address this gap, this study proposes a novel end-to-end model called the Dual Graph Convolutional Networks Integrating Affective Knowledge and Position Information (DGCNAP). This model jointly considers both the contextual features and the affective knowledge information by introducing the affective knowledge from SenticNet into the dependency graph construction of two parallel channels. In addition, a novel multi-target position-aware function is added to the graph convolutional network (GCN) to reduce the impact of noise information and capture the relationships between potential triplets in the same sentence by assigning greater positional weights to words that are in proximity to aspect or opinion terms. The experiment results on the ASTE-Data-V2 datasets demonstrate that our model outperforms other state-of-the-art models significantly, where the F1 scores on 14res, 14lap, 15res, and 16res are 70.72, 57.57, 61.19, and 69.58.<\/jats:p>","DOI":"10.3389\/fnbot.2023.1193011","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T15:01:01Z","timestamp":1692025261000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Dual graph convolutional networks integrating affective knowledge and position information for aspect sentiment triplet extraction"],"prefix":"10.3389","volume":"17","author":[{"given":"Yanbo","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,8,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","article-title":"Enriching word vectors with subword information","volume":"5","author":"Bojanowski","year":"2017","journal-title":"Trans. Assoc. Comput. Linguis"},{"key":"B2","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.neucom.2022.01.021","article-title":"A multi-task learning framework for end-to-end aspect sentiment triplet extraction","volume":"479","author":"Chen","year":"2022","journal-title":"Neurocomputing"},{"key":"B3","doi-asserted-by":"publisher","first-page":"12666","DOI":"10.1609\/aaai.v35i14.17500","article-title":"Bidirectional machine reading comprehension for aspect sentiment triplet extraction","volume":"35","author":"Chen","year":"2021","journal-title":"Proc. Int. AAAI Conf Weblogs"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109506","article-title":"Double embedding and bidirectional sentiment dependence detector for aspect sentiment triplet extraction","author":"Dai","year":"2022","journal-title":"Knowledge Based Syst"},{"key":"B5","doi-asserted-by":"crossref","first-page":"5268","DOI":"10.18653\/v1\/P19-1520","article-title":"\u201cNeural aspect and opinion term extraction with mined rules as weak supervision,\u201d","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Dai","year":"2019"},{"key":"B6","doi-asserted-by":"publisher","first-page":"4171","DOI":"10.48550\/arXiv.1810.04805","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. Proc","volume":"1","author":"Devlin","year":"2019","journal-title":"Conf. North Amer. Chapter Assoc. Comput. Linguistics, Hum. Lang. Technol"},{"key":"B7","first-page":"252","article-title":"\u201cCommon sense computing: From the society of mind to digital intuition and beyond,\u201d","volume-title":"Biometric ID Management and Multimodal Communication","author":"Erik","year":"2009"},{"key":"B8","doi-asserted-by":"publisher","first-page":"2509","DOI":"10.18653\/v1\/N19-1259","article-title":"Target-oriented opinion words extraction with target-fused neural sequence labeling. Proc","volume":"1","author":"Fan","year":"2019","journal-title":"Conf. North Amer. Chapter Assoc. Comput. Linguistics, Hum. Lang. Technol"},{"key":"B9","first-page":"579","article-title":"\u201cExploiting document knowledge for aspect-level sentiment classification,\u201d","author":"He","year":"2018","journal-title":"Annual Meeting of the Association for Computational Linguistics"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1048","article-title":"\u201cAn interactive multi-task learning network for end-to-end aspect-based sentiment analysis,\u201d","author":"He","year":"2019","journal-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"B12","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s12559-022-10078-4","article-title":"Aspect sentiment triplet extraction incorporating syntactic constituency parsing tree and commonsense knowledge graph","volume":"15","author":"Hu","year":"2023","journal-title":"Cognit. Comput"},{"key":"B13","doi-asserted-by":"crossref","first-page":"159","DOI":"10.18653\/v1\/W17-4124","article-title":"\u201cImproving opinion-target extraction with character-level word embeddings,\u201d","volume-title":"Proceedings of the First Workshop on Subword and Character Level Models in NLP","author":"Jebbara","year":"2017"},{"key":"B14","first-page":"1","article-title":"\u201cAspect and opinion terms extraction using double embeddings and attention mechanism for indonesian hotel reviews,\u201d","volume-title":"2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA","author":"Jordhy","year":"2019"},{"key":"B15","first-page":"1051","article-title":"\u201cAdam: A method for stochastic optimization,\u201d","volume-title":"3rd International Conference on Learning Representations","author":"Kingma","year":"2014"},{"key":"B16","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016","journal-title":"arXiv [Preprint]"},{"key":"B17","doi-asserted-by":"publisher","first-page":"6714","DOI":"10.1609\/aaai.v33i01.33016714","article-title":"A unified model for opinion target extraction and target sentiment prediction","volume":"33","author":"Li","year":"2019","journal-title":"Proc. Int. AAAI Conf"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108366","article-title":"A span-sharing joint extraction framework for harvesting aspect sentiment triplets","author":"Li","year":"2022","journal-title":"Knowledge Based Syst"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107643","article-title":"Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks","author":"Liang","year":"2022","journal-title":"Knowledge Based Syst"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110040","article-title":"Egnn: Graph structure learning based on evolutionary computation helps more in graph neural networks","author":"Liu","year":"2023","journal-title":"Appl. Soft Comput"},{"key":"B21","first-page":"4068","article-title":"\u201cInteractive attention networks for aspect-level sentiment classification,\u201d","author":"Ma","year":"2017","journal-title":"Twenty-Sixth International Joint Conference on Artificial Intelligence"},{"key":"B22","first-page":"5876","article-title":"\u201cTargeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive lstm,\u201d","volume-title":"Proceedings of International AAAI Conference","author":"Ma","year":"2018"},{"key":"B23","doi-asserted-by":"crossref","first-page":"9279","DOI":"10.18653\/v1\/2021.emnlp-main.731","article-title":"\u201cPASTE: A tagging-free decoding framework using pointer networks for aspect sentiment triplet extraction,\u201d","volume-title":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","author":"Mukherjee","year":"2021"},{"key":"B24","doi-asserted-by":"publisher","first-page":"8600","DOI":"10.1609\/aaai.v34i05.6383","article-title":"Knowing what, how and why: A near complete solution for aspect-based sentiment analysis","volume":"34","author":"Peng","year":"2020","journal-title":"Proc. Int. AAAI Conf"},{"key":"B25","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.3115\/v1\/D14-1162","article-title":"\u201cGloVe: Global vectors for word representation,\u201d","volume-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP","author":"Pennington","year":"2014"},{"key":"B26","doi-asserted-by":"crossref","first-page":"27","DOI":"10.3115\/v1\/S14-2004","article-title":"\u201cSemeval-2014 task 4: aspect based sentiment analysis,\u201d","volume-title":"Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)","author":"Pontiki","year":"2014"},{"key":"B27","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/j.neucom.2022.07.067","article-title":"Dependency graph enhanced interactive attention network for aspect sentiment triplet extraction","volume":"507","author":"Shi","year":"2022","journal-title":"Neurocomputing"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2022.110001","article-title":"Center-based transfer feature learning with classifier adaptation for surface defect recognition","author":"Shi","year":"2023","journal-title":"Mech. Syst. Signal Process"},{"key":"B29","first-page":"214","article-title":"\u201cAspect level sentiment classification with deep memory network,\u201d","volume-title":"Conference on Empirical Methods in Natural Language Processing","author":"Tang","year":"2016"},{"key":"B30","doi-asserted-by":"publisher","first-page":"12404","DOI":"10.3934\/mbe.2023552","article-title":"Arc fault detection using artificial intelligence: Challenges and benefits","volume":"20","author":"Tian","year":"2023","journal-title":"Math Biosci Eng"},{"key":"B31","first-page":"6000","article-title":"\u201cAttention is all you need,\u201d","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Vaswani","year":"2017"},{"key":"B32","doi-asserted-by":"publisher","first-page":"3316","DOI":"10.1609\/aaai.v31i1.10974","article-title":"Coupled multi-layer attentions for co-extraction of aspect and opinion terms","volume":"31","author":"Wang","year":"2017","journal-title":"Proc. Innov. Appl. Artif. Intell. Conf"},{"key":"B33","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1109\/TCSS.2022.3164719","article-title":"Heterogeneous network representation learning approach for ethereum identity identification","volume":"10","author":"Wang","year":"2023","journal-title":"IEEE Trans. Comput. Soc. Syst"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107736","article-title":"Phrase dependency relational graph attention network for aspect-based sentiment analysis","author":"Wu","year":"2022","journal-title":"Knowledge Based Syst"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.234","article-title":"\u201cGrid tagging scheme for aspect-oriented fine-grained opinion extraction,\u201d","author":"Wu","year":"","journal-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing"},{"key":"B36","doi-asserted-by":"publisher","first-page":"9298","DOI":"10.1609\/aaai.v34i05.6469","article-title":"\u201cLatent opinions transfer network for target-oriented opinion words extraction","volume":"34","author":"Wu","year":"","journal-title":"Proc. Innov. Appl. Artif. Intell. Conf"},{"key":"B37","first-page":"4194","article-title":"\u201cAspect term extraction with history attention and selective transformation,\u201d","author":"Xin","year":"2018","journal-title":"International Joint Conference on Artificial Intelligence"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.183","article-title":"\u201cPosition-aware tagging for aspect sentiment triplet extraction,\u201d","author":"Xu","year":"2020","journal-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing"},{"key":"B39","first-page":"2979","article-title":"\u201cUnsupervised word and dependency path embeddings for aspect term extraction,\u201d","volume-title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","author":"Yin","year":"2016"},{"key":"B40","doi-asserted-by":"publisher","first-page":"4560","DOI":"10.18653\/v1\/D19-1464","article-title":"\u201cAspect-based sentiment classification with aspect-specific graph convolutional networks,\u201d","author":"Zhang","year":"2019","journal-title":"Conf. Empirical Methods Natural Lang. Process (EMNLP) and the 9th Int. Joint Conf. on Natural Lang. Process (IJCNLP"},{"key":"B41","first-page":"819","article-title":"\u201cA multi-task learning framework for opinion triplet extraction,\u201d","volume":"2020","author":"Zhang","year":"2020","journal-title":"Findings of the Association for Computational Linguistics: EMNLP"},{"key":"B42","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/s10844-022-00710-y","article-title":"Enhancing aspect and opinion terms semantic relation for aspect sentiment triplet extraction","volume":"59","author":"Zhang","year":"2022","journal-title":"J. Intell. Inf. Syst"}],"container-title":["Frontiers in Neurorobotics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2023.1193011\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T14:23:19Z","timestamp":1692282199000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2023.1193011\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,14]]},"references-count":42,"alternative-id":["10.3389\/fnbot.2023.1193011"],"URL":"https:\/\/doi.org\/10.3389\/fnbot.2023.1193011","relation":{},"ISSN":["1662-5218"],"issn-type":[{"value":"1662-5218","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,14]]},"article-number":"1193011"}}