{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:40:35Z","timestamp":1760060435802,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB3103600"],"award-info":[{"award-number":["2022YFB3103600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Implicit emotions are often expressed through implicit and weak clues between modalities due to the lack of explicit emotional feature words, representing a significant challenge for multimodal sentiment analysis. In order to improve implicit emotion recognition, this paper proposes a multimodal sentiment analysis method that integrates KAN and the modal dynamic fusion mechanism. This method first introduces the KAN structure to construct a modal feature encoder to enhance the emotional expression ability of features. Then, the emotional contribution weight of each modality is calculated using the difference between the unimodal and multimodal sentiment scores, and the cross-attention mechanism guided by the main modality is used for feature fusion. Experiments on four datasets, CH-SIMS, CH-SIMSv2, MOSI, and MOSEI, show that the proposed method significantly outperforms the mainstream model in multiple indicators, especially when dealing with samples with implicit or ambiguous emotional expressions. The results verify the effectiveness of enhancing feature encoding capabilities and utilizing modal asymmetry information in implicit sentiment analysis.<\/jats:p>","DOI":"10.3390\/sym17091401","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T07:43:16Z","timestamp":1756366996000},"page":"1401","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["KD-MSA: A Multimodal Implicit Sentiment Analysis Approach Based on KAN and Asymmetric Contribution-Aware Dynamic Fusion"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5386-7891","authenticated-orcid":false,"given":"Zhiyuan","family":"Hou","sequence":"first","affiliation":[{"name":"School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Information Research Center of Military Sciences, Academy of Military Sciences, Beijing 100142, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8652-2940","authenticated-orcid":false,"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7267-2509","authenticated-orcid":false,"given":"Ziwei","family":"Lei","sequence":"additional","affiliation":[{"name":"Information Research Center of Military Sciences, Academy of Military Sciences, Beijing 100142, China"}]},{"given":"Zheng","family":"Zeng","sequence":"additional","affiliation":[{"name":"Information Research Center of Military Sciences, Academy of Military Sciences, Beijing 100142, China"}]},{"given":"Ruijun","family":"Jia","sequence":"additional","affiliation":[{"name":"Information Research Center of Military Sciences, Academy of Military Sciences, Beijing 100142, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1109\/JSTSP.2020.2987728","article-title":"Multimodal Intelligence: Representation Learning, Information Fusion, and Applications","volume":"14","author":"Zhang","year":"2020","journal-title":"IEEE J. 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