Leveraging AI for Legal Text Analysis: Case Study-Based Section Prediction

Abstract

This research introduces an artificial intelligence-driven approach for predicting relevant legal sections directly from judicial case texts. Legal documents often contain complex language and unstructured narratives, making manual identification of applicable law sections time-consuming and prone to errors. To address this challenge, the proposed framework employs Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction to transform textual information into numerical vectors, capturing the importance of terms across case documents. Several machine learning classifiers, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Extra Trees (ET), were implemented and rigorously evaluated to determine their effectiveness in section prediction. Comparative analysis reveals that the ET model consistently achieves superior performance in terms of accuracy, precision, recall, and F1-score, demonstrating its robustness and reliability for legal text classification. By leveraging TF-IDF features and ensemble learning techniques, the proposed approach significantly reduces the manual effort required for legal section identification and offers an automated, scalable solution for legal professionals and judicial platforms. This framework not only facilitates faster retrieval of relevant legal provisions but also contributes to the advancement of AI applications in the legal domain, supporting more efficient and informed decision-making processes.