{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:35:42Z","timestamp":1760060142785,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"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":["2022ZD0119501","52374221","ZR2022MF288","ZR2023MF097","23YJAZH192"],"award-info":[{"award-number":["2022ZD0119501","52374221","ZR2022MF288","ZR2023MF097","23YJAZH192"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022ZD0119501","52374221","ZR2022MF288","ZR2023MF097","23YJAZH192"],"award-info":[{"award-number":["2022ZD0119501","52374221","ZR2022MF288","ZR2023MF097","23YJAZH192"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["2022ZD0119501","52374221","ZR2022MF288","ZR2023MF097","23YJAZH192"],"award-info":[{"award-number":["2022ZD0119501","52374221","ZR2022MF288","ZR2023MF097","23YJAZH192"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Education Ministry Humanities and Social Science Research Planning Fund Project of China","award":["2022ZD0119501","52374221","ZR2022MF288","ZR2023MF097","23YJAZH192"],"award-info":[{"award-number":["2022ZD0119501","52374221","ZR2022MF288","ZR2023MF097","23YJAZH192"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Target-oriented opinion words extraction (TOWE) is a novel subtask of aspect-based sentiment analysis (ABSA), which aims to extract opinion words corresponding to a given opinion target within a sentence. In recent years, neural networks have been widely used to solve this problem and have achieved competitive results. However, when faced with complex and long sentences, the existing methods struggle to accurately identify the semantic relationships between distant opinion targets and opinion words. This is primarily because they rely on literal distance, rather than semantic distance, to model the local context or opinion span of the opinion target. To address this issue, we propose a neural network model called DTOWE, which comprises (1) a global module using Inward-LSTM and Outward-LSTM to capture general sentence-level context, and (2) a local module that employs BiLSTM combined with DT-LCF to focus on target-specific opinion spans. DT-LCF is implemented in two ways: DT-LCF-Mask, which uses a binary mask to zero out non-local context beyond a dependency tree distance threshold, \u03b1, and DT-LCF-weight, which applies a dynamic weighted decay to downweigh distant context based on semantic distance. These mechanisms leverage dependency tree structures to measure semantic proximity, reducing the impact of irrelevant words and enhancing the accuracy of opinion span detection. Extensive experiments on four benchmark datasets demonstrate that DTOWE outperforms state-of-the-art models. Specifically, DT-LCF-Weight achieves F1-scores of 73.62% (14lap), 82.24% (14res), 75.35% (15res), and 83.83% (16res), with improvements of 2.63% to 3.44% over the previous state-of-the-art (SOTA) model, IOG. Ablation studies confirm that the dependency tree-based distance measurement and DT-LCF mechanism are critical to the model\u2019s effectiveness, validating their ability to handle complex sentences and capture semantic dependencies between targets and opinion words.<\/jats:p>","DOI":"10.3390\/bdcc9080207","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T13:30:38Z","timestamp":1755091838000},"page":"207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Target-Oriented Opinion Words Extraction Based on Dependency Tree"],"prefix":"10.3390","volume":"9","author":[{"given":"Yan","family":"Wen","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Enhai","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Jiawei","family":"Qu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Lele","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Yuao","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Siyu","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108473","DOI":"10.1016\/j.knosys.2022.108473","article-title":"Aspect-level sentiment analysis with aspect-specific context position information","volume":"243","author":"Huang","year":"2022","journal-title":"Knowl. 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