Prejudge: A Predictive Analytics System for Crime and Legal Judgments


  • Aastha Budhiraja, Kamlesh Sharma


Citation Analysis; Data Classification; Information Retrieval; Legal Domain; Natural Language Processing; Prediction; Similarity Search.


The recent era has seen a substantial inflow of legal documents in the electronic format. Given the fact that data mining can be employed in the world of textual data to extract relevant knowledge, it is being prominently exploited in the domain of criminology and legal matters. With increasing crime rates day-by-day, it has become essential to readily impart justice to the victims. It takes a considerable amount of time for the lawyers to go through previous judgments for their research. The judicial process can be accelerated by decreasing the time spent on research work. Smart legal systems have enormous potential for providing significant insights to the legal community and the general public through the use of legal data. As a result, these systems can assist in the analysis and mitigation of a variety of societal concerns. By extracting numerous things from legal decisions, such as dates, case numbers, reference cases, person names, and so on, this work takes the first step toward realizing a smart legal system. The major research issues in the area of applying machine learning in jurisprudence are information extraction and analysis of legal texts. This study proposes an Machine Learning based framework to improve the user's query for retrieval of precisely relevant legal judgments in order to overcome these limitations. This work has been carried out in order to act as an aid to the legal advisors and the lawyers in framing arguments to make strong standpoints based on predictions given on their case pertaining to previous judicial outcomes for similar such cases. Logistic regression-based classification enables efficient retrieval and prediction by allowing inferences based on domain knowledge collected during the dataset development. According to empirical results obtained, the proposed methodology generates finer results than other traditional approaches.


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Aletras, N., Tsarapatsanis, D., Preoţiuc-Pietro,D., & Lampos, V. (2016). Predicting judicial decisions of the European Court of Human Rights: A natural language processing perspective. PeerJ Computer Science, 2, e93.

D. Huang and W. Lin, "A Model for Legal Judgment Prediction Based on Multi-model Fusion,"2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE), 2019, pp. 892-895, Doi: 10.1109/EITCE47263.2019.9094946.

Evans, O., Stuhlmüller, A., Cundy, C., Carey, R., Kenton, Z., McGrath, T., & Schreiber, A. (2018). Predicting Human Deliberative judgments with Machine Learning. Technical report, University of Oxford.

G. Boella, L. D. Caro, and L. Humphreys, ‘‘Using classification to support legal knowledge engineers in the Eunomos legal document management system,’’ in Proc. 5th Int. Workshop Juris-Inform., 2011, pp. 1–12.

J. Bala, M. Kellar and F. Ramberg, "Predictive analytics for litigation case management,"2017 IEEE International Conference on Big Data (Big Data), 2017, pp. 3826-3830, Doi: 10.1109/BigData.2017.8258384.

Khan, Mohiuddin Ali, Sateesh Kumar Pradhan, and Huda Fatima. "Applying data mining techniques in cybercrimes." 2017 2nd International Conference on Anti-Cyber Crimes (ICACC). IEEE, 2017.

Mugdha Sharma, “Z-Crime: A Data Mining Tool for the Detection of Suspicious Criminal Activities based on the Decision Tree”, International Conference on Data Mining and Intelligent Computing, pp. 1-6, 2014.

N. Aletras, D. Tsarapatsanis, D. Preoţiuc-Pietro, and V. Lampos, ‘‘Predicting judicial decisions of the European Court of Human Rights: A natural language processing perspective,’’ PeerJ Comput. Sci., vol. 2, p. E93, Oct. 2016.

O. Sulea, M. Zampieri, S. Malmasi, M. Vela, L. P. Dinu, and J. van Genabith, “Exploring the use of text classification in the legal domain,” CoRR, vol. abs/1710.09306, 2017. [Online]. Available:

O. Şulea, M. Zampieri, M. Vela, and J. van Genabith, ‘‘Predicting the law area and decisions of French supreme court cases,’’ in Proc. RANLP, Varna, Bulgaria, 2017, pp. 716–722.

R. Nallapati and C. D. Manning, “Legal docket-entry classification: Where machine learning stumbles,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing, ser. EMNLP ’08. Stroudsburg, PA, USA: Association for Computational Linguistics, 2008, pp. 438–446. [Online]. Available: id=1613715.1613771

Stefanie Brüninghaus and Kevin D. Ashley, “Combining Case-Based and Model-Based Reasoning for Predicting the Outcome of Legal Cases” International Conference on Case-Based Reasoning, ICCBR 2003.

Shiju Sathyadevan, M.S. Devan and S. Surya Gangadharan, “Crime Analysis and Prediction using Data Mining”, Proceedings of IEEE 1st International Conference on Networks and Soft Computing, pp. 406-412, 2014.

Tarek Mahfouz and Amr Kandil, “Litigation Outcome Prediction of Differing Site Condition Disputes through Machine Learning Models”, Journal of Computing in Civil Engineering. Volume 26 Issue 3 - May 2012.

W. Lin, T. Kuo, T. Chang, C. Yen, C. Chen, and S. Lin, ‘‘Exploiting machine learning models for Chinese legal documents labeling, case classification, and sentencing prediction,’’ IJCLCLP, vol. 17, no. 4, pp. 49–68, 2012.

Yu-Yueh Huang, Cheng-Te Li and Shyh-Kang Jeng, “Mining Location-based Social Networks for Criminal Activity Prediction”, Proceedings of 24th IEEE International Conference on Wireless and Optical Communication, pp. 185-190, 2015.

Y. Liu and Y. Chen, ‘‘A two-phase sentiment analysis approach for judgment prediction,’’ J. Inf. Sci., vol. 44, no. 5, pp. 594–607, 2018.

Zhong, H., Zhipeng, G., Tu, C., Xiao, C., Liu, Z., & Sun, M. (2018). Legal Judgment Prediction via Topological Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 3540-3549).




How to Cite

Aastha Budhiraja, Kamlesh Sharma. (2024). Prejudge: A Predictive Analytics System for Crime and Legal Judgments. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 648–658. Retrieved from



Research Article