{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T16:44:00Z","timestamp":1764002640031,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,9]],"date-time":"2023-07-09T00:00:00Z","timestamp":1688860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61862061","61563052","61363064","24470","2020GY-093","SK2019-83"],"award-info":[{"award-number":["61862061","61563052","61363064","24470","2020GY-093","SK2019-83"]}]},{"name":"Doctors of Xinjiang University","award":["61862061","61563052","61363064","24470","2020GY-093","SK2019-83"],"award-info":[{"award-number":["61862061","61563052","61363064","24470","2020GY-093","SK2019-83"]}]},{"name":"Shaanxi Provincial Natural Science Foundation","award":["61862061","61563052","61363064","24470","2020GY-093","SK2019-83"],"award-info":[{"award-number":["61862061","61563052","61363064","24470","2020GY-093","SK2019-83"]}]},{"name":"Shangluo City Science and Technology Program Fund Project","award":["61862061","61563052","61363064","24470","2020GY-093","SK2019-83"],"award-info":[{"award-number":["61862061","61563052","61363064","24470","2020GY-093","SK2019-83"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Implicit sentiment identification is a significant classical task in text analysis. Graph neural networks (GNNs) have recently been successful in implicit sentiment identification, but the current approaches still suffer from two problems. On the one hand, there is a lack of structural information carried by the single-view graph structure of implicit sentiment texts to accurately capture obscure sentiment expressions. On the other hand, the predefined fixed graph structure may contain some noisy edges that cannot represent semantic information using an accurate topology, which can seriously impair the performance of implicit sentiment analysis. To address these problems, we introduce a knowledge-fusion-based iterative graph structure learning framework (KIG). Specifically, for the first problem, KIG constructs graph structures based on three views, namely, co-occurrence statistics, cosine similarity, and syntactic dependency trees through prior knowledge, which provides rich multi-source information for implicit sentiment analysis and facilitates the capture of implicit obscure sentiment expressions. To address the second problem, KIG innovatively iterates the three original graph structures and searches for their implicit graph structures to better fit the data themselves to optimize the downstream implicit sentiment analysis task. We compared our method with the mainstream implicit sentiment identification methods on two publicly available datasets, and ours outperformed both benchmark models. The accuracy, recall, and F1 values of KIG on the Pun of the Day dataset reached 89.2%, 93.7%, and 91.1%, respectively. Extensive experimental results demonstrate the superiority of our proposed method for the implicit sentiment identification task.<\/jats:p>","DOI":"10.3390\/s23146257","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T01:02:50Z","timestamp":1688950970000},"page":"6257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Knowledge-Fusion-Based Iterative Graph Structure Learning Framework for Implicit Sentiment Identification"],"prefix":"10.3390","volume":"23","author":[{"given":"Yuxia","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi 830046, China"},{"name":"School of Mathematics and Computer Applications, Shangluo University, Shangluo 726000, China"},{"name":"Engineering Research Center of Qinling Health Welfare Big Data, Universities of Shaanxi Province, Shangluo 726000, China"}]},{"given":"Mahpirat","family":"Mamat","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi 830046, China"},{"name":"Xinjiang Laboratory of Multi-Language Information Technology, \u00dcr\u00fcmqi 830046, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5464-0594","authenticated-orcid":false,"given":"Alimjan","family":"Aysa","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi 830046, China"},{"name":"Xinjiang Laboratory of Multi-Language Information Technology, \u00dcr\u00fcmqi 830046, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7566-6494","authenticated-orcid":false,"given":"Kurban","family":"Ubul","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi 830046, China"},{"name":"Xinjiang Laboratory of Multi-Language Information Technology, \u00dcr\u00fcmqi 830046, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hatzivassiloglou, V., and McKeown, K. 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