Predicting Court Judgment in Criminal Cases by Text Mining Techniques
What is clear is that judges usually judge cases based on their knowledge, experience, personality, and sentiment. Due to high pressures and stress, it may be difficult for them to carefully examine documents and evidence, which leads to more subjective judgments. Legal judgment prediction with arti...
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| Format: | Article |
| Language: | English |
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University of Tehran
2023-04-01
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| Series: | Journal of Information Technology Management |
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| Online Access: | https://jitm.ut.ac.ir/article_92366_a0dc48d4e2a979e7df4d895842b357b0.pdf |
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| author | Mohammad Farhadishad Mohammad Kazemifard Zahra Rezaei |
| author_facet | Mohammad Farhadishad Mohammad Kazemifard Zahra Rezaei |
| author_sort | Mohammad Farhadishad |
| collection | DOAJ |
| description | What is clear is that judges usually judge cases based on their knowledge, experience, personality, and sentiment. Due to high pressures and stress, it may be difficult for them to carefully examine documents and evidence, which leads to more subjective judgments. Legal judgment prediction with artificial intelligence algorithms can benefit judicial bodies, legal experts, and litigants as well as judges. In this research, we are looking at predicting legal sentences in drug cases involving the purchase, possession, concealment, or transportation of illicit drugs, using machine learning methods, and the effect of sentiment and emotions in case texts on predicting the severity of whipping, fines, and imprisonment. So, the text documents of 6000 Persian drug-related cases were pre-processed and then the translation of the NRC Glossary of Emotions and sentiment was used to give each item a score for positive or negative sentiment and a score for emotion. Then machine learning methods were used for modeling. BERT, TFIDF+Adaboost, and Skipgram+LSTM+CNN methods had the highest accuracy, respectively. Also, evaluation criteria were analyzed in situations where sentiment scores, emotional scores, or both were used in the prediction process along with judicial texts. Finally, it was found that the use of sentiment and emotion scores improves the accuracy of legal judgment predictions for all three types of sentences and that sentiments have a greater impact on the accuracy of legal judgment predictions than emotions |
| format | Article |
| id | doaj-art-4fbcd95930484325b88f1727c3b0f262 |
| institution | OA Journals |
| issn | 2008-5893 2423-5059 |
| language | English |
| publishDate | 2023-04-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj-art-4fbcd95930484325b88f1727c3b0f2622025-08-20T02:05:38ZengUniversity of TehranJournal of Information Technology Management2008-58932423-50592023-04-0115220422210.22059/jitm.2023.350464.320692366Predicting Court Judgment in Criminal Cases by Text Mining TechniquesMohammad Farhadishad0Mohammad Kazemifard1Zahra Rezaei2Mas., Department of Computer Engineering and Information technology, Razi University, Kermanshah, Iran,Assistant Prof., Department of Computer Engineering and Information technology, Razi University, Kermanshah, Iran,Assistant Prof., Department of Statistics and Information Technology, Institute of Judiciary, Tehran, Iran,What is clear is that judges usually judge cases based on their knowledge, experience, personality, and sentiment. Due to high pressures and stress, it may be difficult for them to carefully examine documents and evidence, which leads to more subjective judgments. Legal judgment prediction with artificial intelligence algorithms can benefit judicial bodies, legal experts, and litigants as well as judges. In this research, we are looking at predicting legal sentences in drug cases involving the purchase, possession, concealment, or transportation of illicit drugs, using machine learning methods, and the effect of sentiment and emotions in case texts on predicting the severity of whipping, fines, and imprisonment. So, the text documents of 6000 Persian drug-related cases were pre-processed and then the translation of the NRC Glossary of Emotions and sentiment was used to give each item a score for positive or negative sentiment and a score for emotion. Then machine learning methods were used for modeling. BERT, TFIDF+Adaboost, and Skipgram+LSTM+CNN methods had the highest accuracy, respectively. Also, evaluation criteria were analyzed in situations where sentiment scores, emotional scores, or both were used in the prediction process along with judicial texts. Finally, it was found that the use of sentiment and emotion scores improves the accuracy of legal judgment predictions for all three types of sentences and that sentiments have a greater impact on the accuracy of legal judgment predictions than emotionshttps://jitm.ut.ac.ir/article_92366_a0dc48d4e2a979e7df4d895842b357b0.pdflegal judgment predictiontext miningsentiment analysisemotions analysismachine learning |
| spellingShingle | Mohammad Farhadishad Mohammad Kazemifard Zahra Rezaei Predicting Court Judgment in Criminal Cases by Text Mining Techniques Journal of Information Technology Management legal judgment prediction text mining sentiment analysis emotions analysis machine learning |
| title | Predicting Court Judgment in Criminal Cases by Text Mining Techniques |
| title_full | Predicting Court Judgment in Criminal Cases by Text Mining Techniques |
| title_fullStr | Predicting Court Judgment in Criminal Cases by Text Mining Techniques |
| title_full_unstemmed | Predicting Court Judgment in Criminal Cases by Text Mining Techniques |
| title_short | Predicting Court Judgment in Criminal Cases by Text Mining Techniques |
| title_sort | predicting court judgment in criminal cases by text mining techniques |
| topic | legal judgment prediction text mining sentiment analysis emotions analysis machine learning |
| url | https://jitm.ut.ac.ir/article_92366_a0dc48d4e2a979e7df4d895842b357b0.pdf |
| work_keys_str_mv | AT mohammadfarhadishad predictingcourtjudgmentincriminalcasesbytextminingtechniques AT mohammadkazemifard predictingcourtjudgmentincriminalcasesbytextminingtechniques AT zahrarezaei predictingcourtjudgmentincriminalcasesbytextminingtechniques |