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

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.