[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118454-en":3,"doc-seo-118454-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118454,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Enhancing Legal Sentiment Analysis: A Convolutional Neural Network - Long Short-Term Memory Document-Level Model","This research investigates deep learning for sentiment analysis of Canadian maritime case law, focusing on document-level legal analytics and legal information extraction. It proposes a sentiment analysis strategy that combines approaches from legal sentiment analysis with state-of-the-art CNN and LSTM models to surface hidden biases in case law and assess their influence on legal outcomes. The CNN-LSTM framework attains 98.05% accuracy for instance categorization, outperforming SVM (52.57%), naïve Bayes (57.44%), and logistic regression (61.86%), supporting more sophisticated, sentiment-aware AI tools for legal professionals.","machine learning & knowledge extraction  \nArticle  \nEnhancing Legal Sentiment Analysis: A Convolutional Neural Network–Long Short-Term Memory Document-Level Model  \nBolanle Abimbola 1, *, Enrique de La Cal Marin 1 and Qing Tan 2  \nCitation: Abimbola, B.; de La Cal Marin, E.; Tan, Q. Enhancing Legal Sentiment Analysis: A Convolutional Neural Network–Long Short-Term Memory Document-Level Model. Mach. Learn. Knowl. Extr. 2024, 6, 877–897. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)make6020041  \nAcademic Editor: Yoichi Hayashi  \nReceived: 10 February 2024  \nRevised: 6 April 2024  \nAccepted: 7 April 2024  \nPublished: 19 April 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science, University of Oviedo, 33003 Oviedo, Spain  \n2 Faculty of Science and Technology, Athabasca University, 1 University Drive, Athabasca, AB T9S 3A3, Canada; qingt@athabascau.ca  \n* Correspondence: uo285018@uniovi.es  \nAbstract: 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 achievesa 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%, naïve 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.  \nKeywords: convolutional neural networks; deep neural networks; long short-term memory; sentimental analysis; recurrent neural networks  \n1. Introduction  \nIn today's dynamic and interconnected world, the significance of information spans various critical domains, including legal, political, commercial, and individual perspectives, and many more. Recognizing the pivotal role that opinions play in shaping decisions and influencing outcomes, there is a growing need for automated tools to analyze sentiments effectively. Regarding this case, sentiment analysis emerges as a significant participant. Sentiment mining, or sentiment analysis, is a comprehensive natural language processing approach that can identify and classify textual data's emotional tone and subjective content. People are beginning to communicate their thoughts more quickly and in a shorter time, making the manual processing of many viewpoints quite tricky. Therefore, sentiment analysis has proven extremely useful in this field [1–3] . By employing the sentiment analysis technique, stakeholders can also navigate the intricate layers of precedents and decisions, enhancing their capacity for nuanced interpretation and contributing to more informed decision making and policy formulation [2] .  \nRecently, a significant amount of research has b","cbCairYEBlTmafaY","https://ap.wps.com/l/cbCairYEBlTmafaY","pdf",3729990,1,21,"English","en",105,"# Introduction\n## Sentiment analysis and opinion mining\n## Neural networks for sentiment tasks\n# Research Significance","[{\"question\":\"What problem does the study address in legal analytics?\",\"answer\":\"The study targets sentiment analysis for Canadian maritime case law and emphasizes automating legal document extraction at the document level to support more informed policy and analytic decision-making.\"},{\"question\":\"Which model architecture is proposed for sentiment classification?\",\"answer\":\"It combines a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) model to perform document-level sentiment analysis and categorization.\"},{\"question\":\"How does the proposed CNN-LSTM approach compare with traditional machine learning methods?\",\"answer\":\"The CNN-LSTM model reaches 98.05% accuracy, substantially higher than SVM (52.57%), naïve Bayes (57.44%), and logistic regression (61.86%).\"}]","Enhancing Legal Sentiment Analysis: A Convolutional Neural Network - 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