Sentiment Analysis of Canadian Maritime Case Law: A Sentiment Case Law and Deep Learning Approach

Published: 8 May 2023| Version 1 | DOI: 10.17632/7v4jmbjwvc.1
Bola Abimbola


This study employs machine and deep learning methods to expedite legal proceedings to retrieve legal data, classify, review, and predict judgments. Our study adds significantly to the literature because many countries' judicial systems have backlogs that cause delays in justice. The sentiment analysis framework employs deep, distributed, and machine learning to provide access to statutes, laws, and cases, allowing Canadian maritime judges to resolve cases more efficiently. The proposed LSTM+CNN model demonstrated promising results in extracting sentiments and records from various devices and providing practical guidance. As a result, the model can be applied to other systems that adhere to the common-law framework.


Steps to reproduce

1. Download and install Jupyter Notebook (It can be found online and is a platform for running python code) 2. Import all the Libraries that are required for the model. The importation of the library is done using the import statement, and it is critical because it ensures that features are defined. 3. Make sure that the .pynb file and CSV file are in the same folder. 4. Run jupyter notebook. 5. Load the .pynb file to jupyter Notebook. 6. Click on the loaded file to open it. 7. Click trust to make the python file runnable. 8. If all libraries are not installed, you will see the warning. Go ahead and install the missing libraries. 9. Click run in every section to reproduce the experiment.


Universidad de Oviedo, Athabasca University


Machine Learning, Sentiment Analysis