{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T22:30:49Z","timestamp":1772058649030,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T00:00:00Z","timestamp":1713484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>This research investigates the application of deep learning in sentiment analysis of Canadian maritime case law. It offers a framework for improving maritime law and legal analytic policy-making procedures. The automation of legal document extraction takes center stage, underscoring the vital role sentiment analysis plays at the document level. Therefore, this study introduces a novel strategy for sentiment analysis in Canadian maritime case law, combining sentiment case law approaches with state-of-the-art deep learning techniques. The overarching goal is to systematically unearth hidden biases within case law and investigate their impact on legal outcomes. Employing Convolutional Neural Network (CNN)- and long short-term memory (LSTM)-based models, this research achieves a remarkable accuracy of 98.05% for categorizing instances. In contrast, conventional machine learning techniques such as support vector machine (SVM) yield an accuracy rate of 52.57%, na\u00efve Bayes at 57.44%, and logistic regression at 61.86%. The superior accuracy of the CNN and LSTM model combination underscores its usefulness in legal sentiment analysis, offering promising future applications in diverse fields like legal analytics and policy design. These findings mark a significant choice for AI-powered legal tools, presenting more sophisticated and sentiment-aware options for the legal profession.<\/jats:p>","DOI":"10.3390\/make6020041","type":"journal-article","created":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T04:10:15Z","timestamp":1713499815000},"page":"877-897","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Enhancing Legal Sentiment Analysis: A Convolutional Neural Network\u2013Long Short-Term Memory Document-Level Model"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9554-8551","authenticated-orcid":false,"given":"Bolanle","family":"Abimbola","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Oviedo, 33003 Oviedo, Spain"}]},{"given":"Enrique","family":"de La Cal Marin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Oviedo, 33003 Oviedo, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6447-2133","authenticated-orcid":false,"given":"Qing","family":"Tan","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Athabasca University, 1 University Drive, Athabasca, AB T9S 3A3, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,19]]},"reference":[{"key":"ref_1","unstructured":"Liu, B. 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