Legal Judgment Prediction using Natural Language Processing and Machine Learning Methods: A Systematic Literature Review

Nasa Zata Dina, Sri Devi Ravana and Norisma Idris

SAGE Open, 2025, vol. 15, issue 2, 21582440251329663

Abstract: Legal Judgment Prediction (LJP) study is experiencing a growing need for automating legal judgment process to predict court decisions. In this context, the present paper provides a systematic literature review of previous LJP study, implementing machine learning (ML) as decision-making and natural language processing (NLP) to extract information from legal judgment documents. Relevant articles were found in reputable indexing databases through the search strategy, with the outcomes filtered by applying inclusion and exclusion criteria. Furthermore, six research questions were constructed to observe the datasets, topics/trends, NLP and ML methods, evaluation methods, and challenges. The LJP topic included three topics which were charge, law article, and term-of-penalty prediction. There were 21 NLP methods applied, emphasizing the highest implementation of Term Frequency-Inverse Document Frequency (TF-IDF) while the most implemented ML method was Support Vector Machine (SVM). Accuracy was the most used metric as an evaluation method. Additionally, this work emphasizes the importance of LJP and the potential use of NLP and ML. This study urges further investigation into NLP and ML, as well as practical uses of LJP. Low classification performance, low quantity of data, imbalanced dataset, data accessibility, data labeling, extraction of semantic information from natural language, expert involvement, generalizability issue, and multilingual datasets represent a few of the major problems that LJP faces, and the study is significant because it clarifies some of the major issues that LJP faces. Among those problems, low amounts of dataset and low classification performance were regarded as the most challenging tasks to deal with.

Keywords: legal judgment prediction; legal judgment document; machine learning; natural language processing (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/21582440251329663 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:15:y:2025:i:2:p:21582440251329663

DOI: 10.1177/21582440251329663

Access Statistics for this article

More articles in SAGE Open
Bibliographic data for series maintained by SAGE Publications ().