{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T19:02:59Z","timestamp":1769713379332,"version":"3.49.0"},"reference-count":2,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>Traditional graph convolutional neural networks (GCN) utilizing linear feature combination methods have limited capacity to capture the interaction between complex features. While current research has extensively investigated various syntactic dependency tree structures, the optimization of GCN algorithms has often been overlooked, leading to suboptimal efficiency in practical applications. To address this issue, this paper proposes a cross-feature method that utilizes feature vector multiplication to construct non-linear combinations of GCN features and enhance the model\u2019s capability to extract complex feature correlations. Experimental results demonstrate the superiority of the proposed method, with our models outperforming state-of-the-art methods and achieving significant improvements on three standard benchmark datasets. These results suggest that the cross-feature method can effectively extract potential connections between features, highlighting its potential for improving the performance of GCN-based models in real-world applications.<\/jats:p>","DOI":"10.3233\/jifs-221687","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T11:33:28Z","timestamp":1690284808000},"page":"9421-9432","source":"Crossref","is-referenced-by-count":0,"title":["Cross feature enhanced graph convolutional network for aspect-based sentiment analysis"],"prefix":"10.1177","volume":"45","author":[{"given":"Longji","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Software, Xinjiang University, Urumqi Xinjiang, China"}]},{"given":"Hui","family":"zhao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-221687_ref28","doi-asserted-by":"crossref","unstructured":"Pennington J. , Socher R. and Manning C.D. , GloVe: Global vectors for word representation, In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014) 14 (2014), pp. 1532\u20131543.","DOI":"10.3115\/v1\/D14-1162"},{"key":"10.3233\/JIFS-221687_ref29","doi-asserted-by":"crossref","first-page":"49","DOI":"10.3115\/v1\/P14-2009","article-title":"Adaptive recursive neural network for targetdependent twitter sentiment classification","volume":"2","author":"Dong","year":"2014","journal-title":"In Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 2: Short papers)"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-221687","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T08:27:40Z","timestamp":1769675260000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-221687"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"references-count":2,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-221687","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,2]]}}}