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The model combines multi-bandwidth pooling operations with locally enhanced linear embedding techniques to amplify textual local features. Additionally, it leverages BERT\u2019s multi-head attention mechanism to capture nuanced semantic information, thereby enriching the model\u2019s contextual representations. Furthermore, to address the challenge of imbalanced sample distributions, the model incorporates a novel Dynamic Balance Representation (DBR) joint training strategy. This strategy simultaneously optimizes cross-entropy loss and focal loss, enabling the model to adapt more effectively to complex data distributions. Consequently, the model\u2019s generalization ability and classification accuracy are significantly improved. Experiments on three public datasets demonstrate that the proposed MLFE-BERT model, with its MLFE and joint training strategy, outperforms the baseline BERT model in sentiment classification tasks. The results show consistent improvements in precision, [Formula: see text]-score and recall, confirming the feasibility and effectiveness of the proposed method.<\/jats:p>","DOI":"10.1142\/s0218126625503797","type":"journal-article","created":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T12:38:24Z","timestamp":1752064704000},"source":"Crossref","is-referenced-by-count":0,"title":["A Multi-Bandwidth Local Feature Enhanced Sentiment Analysis Model Based on BERT"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0222-860X","authenticated-orcid":false,"given":"Ping","family":"Huang","sequence":"first","affiliation":[{"name":"College of Computer and Artificial Intelligence, Nanjing University of Science and Technology, ZiJin College, No. 89 Wenlan Road, Nanjing, Jiangsu 210023, P. R. 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