{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T10:58:27Z","timestamp":1781693907300,"version":"3.54.5"},"reference-count":26,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Henan 2026 Science and Technology Development Plan (II) Soft Science Research Project","award":["262400410282"],"award-info":[{"award-number":["262400410282"]}]},{"name":"Henan Provincial Department of Education 2026 Key Research Project of Henan Higher Education Institutions","award":["26B520015"],"award-info":[{"award-number":["26B520015"]}]},{"name":"Key Science and Technology Project of Henan Province University","award":["26B413002"],"award-info":[{"award-number":["26B413002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>To address the limitations of existing sentiment analysis methods\u2014specifically, the suboptimal performance of equal-weight ensemble averaging\u2014this paper proposes an improved weighted average ensemble method for textual sentiment analysis. The ensemble method determines model weights by averaging validation accuracies over the final ten training epochs (41\u201350). Critically, the weights are normalized to ensure that the fused probability lies in [0,1]. The Transformer and FNet models are fused as base classifiers, leveraging Transformer\u2019s global semantic capture capability and FNet\u2019s high efficiency in sequence processing. Experimental results on a dataset of 8000 online comments related to the employment prospects of text data-related majors demonstrate that the proposed method achieves an AUC of 92.4%, an accuracy of 91.2%, and an F1-score of 91.0%. The method also achieves an inference speed of 32 samples\/son CPU, offering a favorable trade-off between performance and efficiency for resource-constrained scenarios.<\/jats:p>","DOI":"10.3390\/info17060605","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T10:19:53Z","timestamp":1781691593000},"page":"605","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Improved Weighted Average Ensemble Method for Textual Sentiment Analysis with Transformer and FNet"],"prefix":"10.3390","volume":"17","author":[{"given":"Xia","family":"Wu","sequence":"first","affiliation":[{"name":"School of Information Engineering, Henan Vocational College of Water Conservancy and Environment, Zhengzhou 450008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongxi","family":"Ke","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4114-497X","authenticated-orcid":false,"given":"Qinge","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"ref_1","unstructured":"CNNIC (2024). 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