Enhancing Legal Sentiment Analysis: A CNN-LSTM Document-Level Model

Published: 9 February 2024| Version 1 | DOI: 10.17632/s3r4thpy95.1
Bola Abimbola


This research delves into the use of deep learning methods in analyzing sentiment within Canadian maritime case law. Its primary aim is to apply these advanced techniques to the fields of legal analytics and policy development. The study underscores the crucial role of Artificial Intelligence (AI) in both legal practices and policy formulation, drawing comparisons among various machine learning models. The results of this research indicate that AI has the capability to foster a more nuanced and well-informed legal landscape, underscoring its potential in legal practices. The investigation into case adjudication in Canadian maritime law has unveiled significant insights, particularly in terms of trial outcomes related to the number of judges presiding. It was found that when a single judge was responsible for a case, the probability of a guilty verdict was 46%, with a not guilty verdict being slightly more likely at approximately 51%. This demonstrates an almost equal distribution of case outcomes, with around 3% of cases left unresolved. Interestingly, the occurrence of unresolved verdicts did not show a notable increase with the involvement of three judges, as it rose only slightly to 5%. This suggests that the addition of more judges to the trial does not significantly affect the rate of indecision in verdicts, pointing to a remarkable consistency in judgment outcomes across different judicial settings within the domain of Canadian maritime law. The term 'accuracy' in this context refers to the rate at which a model correctly predicts outcomes.


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Universidad de Oviedo


Artificial Intelligence