Abstract
As the amount of historical data available in the legal arena has grown over time, industry specialists are driven to gather, compile, and analyze this data in order to forecast court case rulings. However, predicting and justifying court rulings while using judicial facts is no easy task. Currently, previous research on forecasting court outcomes using small experimental datasets yielded a number of unanticipated predictions utilizing machine learning (ML) models and conventional methodologies for categorical feature encoding. The current work proposes forecasting court judgments using a hybrid neural network model, namely a long short-term memory (LSTM) network with a CNN, in order to effectively forecast court rulings using historic judicial datasets. By prioritizing and choosing features that scored the highest in the provided legal data set, only the most pertinent features were picked. After that, the LSTM+CNN model was utilized to forecast lawsuit verdicts. In contrast to previous related experiments, this composite model’s testing results were promising, showing 92.05 percent accuracy, 93 percent precision, 94 percent recall, and a 93 percent F1-score.
Highlights
With the introduction of artificial intelligence, data mining applications have become increasingly frequently used in a variety of fields, including commerce, academia, medicine, and litigation
To overcome the shortcomings of this baseline study [1], we developed an efficient feature selection approach augmented with a deep learning model (LSTM+convolutional neural network (CNN)), which was previously effectively used in a wide range of applications, including personality classification [3], plant disease classification [6], concept-level sentiment analysis [9,10], and others [2]
The suggested approach increases the performance of the model for court case judgments by a substantial margin
Summary
With the introduction of artificial intelligence, data mining applications have become increasingly frequently used in a variety of fields, including commerce, academia, medicine, and litigation. Computational approaches, such as those used in other disciplines, allow the law sector to gather and evaluate a large amount of legal data available in judiciary archives. Examining such judiciary data to forecast legal outcomes reduces the strain on court employees and allows for prompt decision-making, resulting in the effective processing of cases [1]. This may assist non-legal specialists in comprehending the fundamentals of a certain case or situation [2]
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