{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T19:39:45Z","timestamp":1777923585509,"version":"3.51.4"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Natural Science Foundation of China under Grants","award":["61836016"],"award-info":[{"award-number":["61836016"]}]},{"name":"National Natural Science Foundation of China under Grants","award":["61836016"],"award-info":[{"award-number":["61836016"]}]},{"name":"National Natural Science Foundation of China under Grants","award":["61836016"],"award-info":[{"award-number":["61836016"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Inf Syst"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s10844-023-00811-2","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T11:04:26Z","timestamp":1693566266000},"page":"163-189","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Enhancing aspect-based sentiment analysis with dependency-attention GCN and mutual assistance mechanism"],"prefix":"10.1007","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4437-9599","authenticated-orcid":false,"given":"Jialin","family":"Feng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyi","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,1]]},"reference":[{"key":"811_CR1","doi-asserted-by":"publisher","unstructured":"Bekoulis, G., Deleu, J., & Demeester, T., et al (2018). Joint entity recognition and relation extraction as a multi-head selection problem. Expert Systems with Applications 114, 34\u201345. https:\/\/doi.org\/10.1016\/j.eswa.2018.07.032","DOI":"10.1016\/j.eswa.2018.07.032"},{"key":"811_CR2","doi-asserted-by":"publisher","unstructured":"Bu, J., Ren, L., & Zheng, S., et al (2021). Asap: A chinese review dataset towards aspect category sentiment analysis and rating prediction. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 2069\u20132079. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.167","DOI":"10.18653\/v1\/2021.naacl-main.167"},{"key":"811_CR3","doi-asserted-by":"publisher","unstructured":"Cai, H., Xia, R., & Yu, J. (2021). Aspect-category-opinion-sentiment quadruple extraction with implicit aspects and opinions. In: 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), pp 340\u2013350. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.29","DOI":"10.18653\/v1\/2021.acl-long.29"},{"key":"811_CR4","doi-asserted-by":"publisher","unstructured":"Chen, S., Liu, J., & Wang, Y., et al (2020). Synchronous double\u2013channel recurrent network for aspect\u2013opinion pair extraction. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 6515\u20136524. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.582","DOI":"10.18653\/v1\/2020.acl-main.582"},{"key":"811_CR5","doi-asserted-by":"publisher","unstructured":"Chen, S., Wang, Y., & Liu, J., et\u00a0al (2021). Bidirectional machine reading comprehension for aspect sentiment triplet extraction. In: Proceedings of the AAAI conference on artificial intelligence, pp 12666\u201312674. https:\/\/doi.org\/10.1609\/aaai.v35i14.17500","DOI":"10.1609\/aaai.v35i14.17500"},{"key":"811_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Y., Keming, C., Sun, X., et\u00a0al (2022). A span\u2013level bidirectional network for aspect sentiment triplet extraction. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp 4300\u20134309","DOI":"10.18653\/v1\/2022.emnlp-main.289"},{"key":"811_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Z., & Qian, T. (2020). Relation\u2013aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 3685\u20133694. 10.18653\/v1\/2020.acl-main.340","DOI":"10.18653\/v1\/2020.acl-main.340"},{"issue":"2","key":"811_CR8","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1162\/coli\\_a_00402","volume":"47","author":"MC De Marneffe","year":"2021","unstructured":"De Marneffe, M. C., Manning, C. D., Nivre, J., et al. (2021). Universal dependencies. Computational Linguistics, 47(2), 255\u2013308. https:\/\/doi.org\/10.1162\/coli_a_00402","journal-title":"Computational Linguistics"},{"key":"811_CR9","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M. W., & Lee, K., et\u00a0al (2019). Bert: Pre\u2013training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, volume 1 (Long and Short Papers), pp 4171\u20134186. https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"811_CR10","doi-asserted-by":"publisher","unstructured":"Fan, Z., Wu, Z., Dai, X., et\u00a0al (2019). Target\u2013oriented opinion words extraction with target-fused neural sequence labeling. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, volume 1 (Long and Short Papers), pp 2509\u20132518. https:\/\/doi.org\/10.18653\/v1\/N19-1259","DOI":"10.18653\/v1\/N19-1259"},{"key":"811_CR11","doi-asserted-by":"publisher","unstructured":"He, R., Lee, W. S., & Ng, H. T., et\u00a0al (2019). An interactive multi\u2013task learning network for end-to-end aspect-based sentiment analysis. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 504\u2013515. https:\/\/doi.org\/10.18653\/v1\/P19-1048","DOI":"10.18653\/v1\/P19-1048"},{"key":"811_CR12","doi-asserted-by":"publisher","unstructured":"Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 168\u2013177. https:\/\/doi.org\/10.1145\/1014052.1014073","DOI":"10.1145\/1014052.1014073"},{"key":"811_CR13","doi-asserted-by":"publisher","unstructured":"Hu, M., Peng, Y., & Huang Z, et\u00a0al (2019). Open\u2013domain targeted sentiment analysis via span\u2013based extraction and classification. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 537\u2013546. https:\/\/doi.org\/10.18653\/v1\/P19-1051","DOI":"10.18653\/v1\/P19-1051"},{"key":"811_CR14","doi-asserted-by":"publisher","unstructured":"Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv:1609.02907. https:\/\/doi.org\/10.48550\/arXiv.1609.02907","DOI":"10.48550\/arXiv.1609.02907"},{"key":"811_CR15","unstructured":"Klinger, R., & Cimiano, P. (2013a). Bi\u2013directional inter\u2013dependencies of subjective expressions and targets and their value for a joint model. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (volume 2: Short Papers), pp 848\u2013854"},{"key":"811_CR16","doi-asserted-by":"publisher","unstructured":"Klinger, R., Cimiano, P. (2013b). Joint and pipeline probabilistic models for fine\u2013grained sentiment analysis: Extracting aspects, subjective phrases and their relations. In: 2013 IEEE 13th International Conference on Data Mining Workshops, IEEE, pp 937\u2013944. https:\/\/doi.org\/10.1109\/ICDMW.2013.13","DOI":"10.1109\/ICDMW.2013.13"},{"key":"811_CR17","doi-asserted-by":"publisher","unstructured":"Li, X., Bing, L., & Li, P., et\u00a0al (2019). A unified model for opinion target extraction and target sentiment prediction. In: Proceedings of the AAAI conference on artificial intelligence, pp 6714\u20136721, https:\/\/doi.org\/10.1609\/aaai.v33i01.33016714","DOI":"10.1609\/aaai.v33i01.33016714"},{"key":"811_CR18","doi-asserted-by":"publisher","unstructured":"Li, Y., Lin, Y., & Lin, Y., et\u00a0al (2022a) .A span\u2013sharing joint extraction framework for harvesting aspect sentiment triplets. Knowledge\u2013Based Systems 242, 108366. https:\/\/doi.org\/10.1016\/j.knosys.2022.108366","DOI":"10.1016\/j.knosys.2022.108366"},{"issue":"5","key":"811_CR19","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1007\/s10115-022-01675-8","volume":"64","author":"Y Li","year":"2022","unstructured":"Li, Y., Wang, C., Lin, Y., et al. (2022). Span-based relational graph transformer network for aspect-opinion pair extraction. Knowledge and Information Systems, 64(5), 1305\u20131322. https:\/\/doi.org\/10.1007\/s10115-022-01675-8","journal-title":"Knowledge and Information Systems"},{"issue":"1","key":"811_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00416ED1V01Y201204HLT016","volume":"5","author":"B Liu","year":"2012","unstructured":"Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1\u2013167. https:\/\/doi.org\/10.2200\/S00416ED1V01Y201204HLT016","journal-title":"Synthesis Lectures on Human Language Technologies"},{"key":"811_CR21","doi-asserted-by":"publisher","unstructured":"Liu, P., Joty, S., & Meng, H. (2015). Fine\u2013grained opinion mining with recurrent neural networks and word embeddings. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 1433\u20131443. https:\/\/doi.org\/10.18653\/v1\/D15-1168","DOI":"10.18653\/v1\/D15-1168"},{"key":"811_CR22","doi-asserted-by":"publisher","unstructured":"Ma, D., Li, S., & Wu, F., et\u00a0al (2019). Exploring sequence\u2013to\u2013sequence learning in aspect term extraction. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 3538\u20133547. https:\/\/doi.org\/10.18653\/v1\/P19-1344","DOI":"10.18653\/v1\/P19-1344"},{"key":"811_CR23","doi-asserted-by":"publisher","unstructured":"Nazir, A., Rao, Y. (2022). Iaotp: An interactive end\u2013to\u2013end solution for aspect\u2013opinion term pairs extraction. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1588\u20131598. https:\/\/doi.org\/10.1145\/3477495.3532085","DOI":"10.1145\/3477495.3532085"},{"key":"811_CR24","doi-asserted-by":"publisher","unstructured":"Peng, H., Xu, L., & Bing, L., et\u00a0al (2020). Knowing what, how and why: A near complete solution for aspect\u2013based sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 8600\u20138607. https:\/\/doi.org\/10.1609\/aaai.v34i05.6383","DOI":"10.1609\/aaai.v34i05.6383"},{"key":"811_CR25","doi-asserted-by":"publisher","unstructured":"Pontiki, M., Galanis, D., & Pavlopoulos, J., et\u00a0al (2014). Semeval\u20132014 task 4: Aspect based sentiment analysis. In: 8th International Workshop on Semantic Evaluation August 23\u201324, 2014., pp 27\u201335. https:\/\/doi.org\/10.3115\/v1\/s14-2004","DOI":"10.3115\/v1\/s14-2004"},{"key":"811_CR26","doi-asserted-by":"publisher","unstructured":"Pontiki, M., Galanis, D., & Papageorgiou, H., et\u00a0al (2015). Semeval\u20132015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015), pp 486\u2013495. https:\/\/doi.org\/10.18653\/v1\/s15-2082","DOI":"10.18653\/v1\/s15-2082"},{"key":"811_CR27","doi-asserted-by":"publisher","unstructured":"Pontiki, M., Galanis, D., & Papageorgiou, H., et\u00a0al (2016). Semeval\u20132016 task 5: Aspect based sentiment analysis. In: ProWorkshop on Semantic Evaluation (SemEval\u20132016), Association for Computational Linguistics, pp 19\u201330. https:\/\/doi.org\/10.18653\/v1\/s16-1055","DOI":"10.18653\/v1\/s16-1055"},{"key":"811_CR28","doi-asserted-by":"publisher","unstructured":"Popescu, A. M., & Etzioni, O. (2007). Extracting product features and opinions from reviews. Natural Language Processing and Text Mining pp 9\u201328. https:\/\/doi.org\/10.1007\/978-1-84628-754-1_2","DOI":"10.1007\/978-1-84628-754-1_2"},{"issue":"1","key":"811_CR29","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1162\/coli_a_00034","volume":"37","author":"G Qiu","year":"2011","unstructured":"Qiu, G., Liu, B., Bu, J., et al. (2011). Opinion word expansion and target extraction through double propagation. Computational Linguistics, 37(1), 9\u201327. https:\/\/doi.org\/10.1162\/coli_a_00034","journal-title":"Computational Linguistics"},{"key":"811_CR30","doi-asserted-by":"publisher","unstructured":"Ruder, S. (2017). An overview of multi-task learning in deep neural networks. arXiv:1706.05098. https:\/\/doi.org\/10.48550\/arXiv.1706.05098","DOI":"10.48550\/arXiv.1706.05098"},{"key":"811_CR31","doi-asserted-by":"publisher","unstructured":"Sun, K., Zhang, R., & Mensah, S., et\u00a0al (2019). Aspect\u2013level sentiment analysis via convolution over dependency tree. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 5679\u20135688. https:\/\/doi.org\/10.18653\/v1\/D19-1569","DOI":"10.18653\/v1\/D19-1569"},{"key":"811_CR32","doi-asserted-by":"publisher","unstructured":"Tang, D., Qin, B., & Feng, X., et\u00a0al (2016). Effective lstms for target\u2013dependent sentiment classification. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp 3298\u20133307. https:\/\/doi.org\/10.48550\/arXiv.1512.01100","DOI":"10.48550\/arXiv.1512.01100"},{"key":"811_CR33","doi-asserted-by":"publisher","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., & Casanova, A., et\u00a0al (2017). Graph attention networks. arXiv:2110.08020. https:\/\/doi.org\/10.48550\/arXiv.1710.10903","DOI":"10.48550\/arXiv.1710.10903"},{"key":"811_CR34","doi-asserted-by":"publisher","unstructured":"Wan, H., Yang, Y., Du, J., et\u00a0al (2020). Target\u2013aspect\u2013sentiment joint detection for aspect-based sentiment analysis. In: Proceedings of the AAAI conference on artificial intelligence, pp 9122\u20139129. https:\/\/doi.org\/10.1609\/aaai.v34i05.6447","DOI":"10.1609\/aaai.v34i05.6447"},{"key":"811_CR35","doi-asserted-by":"publisher","unstructured":"Wan, Y., Chen, Y., & Shi, L., et\u00a0al (2022). A knowledge\u2013enhanced interactive graph convolutional network for aspect\u2013based sentiment analysis. Journal of Intelligent Information Systems pp 1\u201323. https:\/\/doi.org\/10.1007\/s10844-022-00761-1","DOI":"10.1007\/s10844-022-00761-1"},{"key":"811_CR36","doi-asserted-by":"publisher","unstructured":"Wang, W., Pan, S. J., & Dahlmeier, D., et\u00a0al (2017). Coupled multi\u2013layer attentions for co\u2013extraction of aspect and opinion terms. In: Proceedings of the Thirty\u2013First AAAI Conference on Artificial Intelligence, pp 3316\u20133322. https:\/\/doi.org\/10.1609\/aaai.v31i1.10974","DOI":"10.1609\/aaai.v31i1.10974"},{"key":"811_CR37","doi-asserted-by":"publisher","unstructured":"Wang, X., Liu, P., & Zhu, Z., et\u00a0al (2022). Aspect\u2013based sentiment analysis with graph convolutional networks over dependency awareness. In: 2022 26th International Conference on Pattern Recognition (ICPR), IEEE, pp 2238\u20132245. https:\/\/doi.org\/10.1109\/ICPR56361.2022.9956479","DOI":"10.1109\/ICPR56361.2022.9956479"},{"key":"811_CR38","doi-asserted-by":"publisher","unstructured":"Wei, Z., Hong, Y., & Zou, B., et\u00a0al (2020). Don\u2019t eclipse your arts due to small discrepancies: Boundary repositioning with a pointer network for aspect extraction. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 3678\u20133684. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.339","DOI":"10.18653\/v1\/2020.acl-main.339"},{"key":"811_CR39","doi-asserted-by":"publisher","unstructured":"Wu, Z., Ying, C., & Zhao, F., et\u00a0al (2020). Grid tagging scheme for aspect\u2013oriented fine-grained opinion extraction. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 2576\u20132585. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.234","DOI":"10.18653\/v1\/2020.findings-emnlp.234"},{"key":"811_CR40","doi-asserted-by":"publisher","unstructured":"Xu, H., Liu, B., & Shu, L., et\u00a0al (2018). Double embeddings and cnn\u2013based sequence labeling for aspect extraction. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (volume 2: Short Papers), pp 592\u2013598. https:\/\/doi.org\/10.18653\/v1\/P18-2094","DOI":"10.18653\/v1\/P18-2094"},{"key":"811_CR41","doi-asserted-by":"publisher","unstructured":"Xu, L., Li, H., & Lu, W., et\u00a0al (2020). Position\u2013aware tagging for aspect sentiment triplet extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 2339\u20132349. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.183","DOI":"10.18653\/v1\/2020.emnlp-main.183"},{"key":"811_CR42","doi-asserted-by":"publisher","unstructured":"Yan, H., Dai, J., & Ji, T., et\u00a0al (2021). A unified generative framework for aspect-based sentiment analysis. In: 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), pp 2416\u20132429. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.188","DOI":"10.18653\/v1\/2021.acl-long.188"},{"key":"811_CR43","unstructured":"Yang, B., & Cardie, C. (2012). Extracting opinion expressions with semi\u2013markov conditional random fields. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp 1335\u20131345"},{"key":"811_CR44","unstructured":"Yang, B., & Cardie, C. (2013). Joint inference for fine-grained opinion extraction. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (volume 1: Long Papers), pp 1640\u20131649"},{"key":"811_CR45","doi-asserted-by":"publisher","unstructured":"Ye, H., Yan, Z., & Luo, Z., et\u00a0al (2017). Dependency\u2013tree based convolutional neural networks for aspect term extraction. In: Advances in Knowledge Discovery and Data Mining: 21st Pacific\u2013Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part II 21, Springer, pp 350\u2013362. https:\/\/doi.org\/10.1007\/978-3-319-57529-2_28","DOI":"10.1007\/978-3-319-57529-2_28"},{"key":"811_CR46","doi-asserted-by":"publisher","unstructured":"Yin, Y., Wei, F., & Dong, L., et\u00a0al (2016). Unsupervised word and dependency path embeddings for aspect term extraction. In: Proceedings of the Twenty\u2013Fifth International Joint Conference on Artificial Intelligence, pp 2979\u20132985. https:\/\/doi.org\/10.48550\/arXiv.1605.07843","DOI":"10.48550\/arXiv.1605.07843"},{"key":"811_CR47","doi-asserted-by":"publisher","unstructured":"Yu, P., Tan, W., & Niu, W., et\u00a0al (2022). Aspect\u2013location attention networks for aspect\u2013category sentiment analysis in social media. Journal of Intelligent Information Systems, pp 1\u201325. https:\/\/doi.org\/10.1007\/s10844-022-00760-2","DOI":"10.1007\/s10844-022-00760-2"},{"key":"811_CR48","doi-asserted-by":"publisher","unstructured":"Zhang C, Li Q, Song D, et\u00a0al (2020) A multi-task learning framework for opinion triplet extraction. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 819\u2013828. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.72","DOI":"10.18653\/v1\/2020.findings-emnlp.72"},{"issue":"2","key":"811_CR49","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/s10844-022-00710-y","volume":"59","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., Ding, Q., Zhu, Z., et al. (2022). Enhancing aspect and opinion terms semantic relation for aspect sentiment triplet extraction. Journal of Intelligent Information Systems, 59(2), 523\u2013542. https:\/\/doi.org\/10.1007\/s10844-022-00710-y","journal-title":"Journal of Intelligent Information Systems"},{"key":"811_CR50","doi-asserted-by":"publisher","unstructured":"Zhao, H., Huang, L., & Zhang, R., et\u00a0al (2020). Spanmlt: A span\u2013based multi\u2013task learning framework for pair\u2013wise aspect and opinion terms extraction. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 3239\u20133248. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.296","DOI":"10.18653\/v1\/2020.acl-main.296"},{"key":"811_CR51","doi-asserted-by":"publisher","unstructured":"Zhou, X., Wan, X., & Xiao, J. (2015). Representation learning for aspect category detection in online reviews. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp 417\u2013423. https:\/\/doi.org\/10.1609\/aaai.v29i1.9194","DOI":"10.1609\/aaai.v29i1.9194"},{"key":"811_CR52","doi-asserted-by":"publisher","unstructured":"Zhuang, L., Jing, F., Zhu, X. Y. (2006). Movie review mining and summarization. In: Proceedings of the 15th ACM international conference on Information and knowledge management, pp 43\u201350. https:\/\/doi.org\/10.1145\/1183614.1183625","DOI":"10.1145\/1183614.1183625"}],"container-title":["Journal of Intelligent Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-023-00811-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10844-023-00811-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-023-00811-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T18:14:43Z","timestamp":1709835283000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10844-023-00811-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,1]]},"references-count":52,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["811"],"URL":"https:\/\/doi.org\/10.1007\/s10844-023-00811-2","relation":{},"ISSN":["0925-9902","1573-7675"],"issn-type":[{"value":"0925-9902","type":"print"},{"value":"1573-7675","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,1]]},"assertion":[{"value":"11 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The authors declare that there is no competing interests with anybody or any institution regarding the publication of this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}