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مدیریت فناوری اطلاعات، جلد ۱۵، شماره ۲، صفحات ۲۰۴-۲۲۲
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Predicting Court Judgment in Criminal Cases by Text Mining Techniques |
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| چکیده انگلیسی مقاله |
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 |
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| کلیدواژه‌های انگلیسی مقاله |
Legal Judgment Prediction,Text mining,Sentiment analysis,Emotions Analysis,Machine learning |
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| نویسندگان مقاله |
Mohammad Farhadishad |
Mas., Department of Computer Engineering and Information technology, Razi University, Kermanshah, Iran,
Mohammad Kazemifard |
Assistant Prof., Department of Computer Engineering and Information technology, Razi University, Kermanshah, Iran,
Zahra Rezaei |
Assistant Prof., Department of Statistics and Information Technology, Institute of Judiciary, Tehran, Iran,
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| نشانی اینترنتی |
https://jitm.ut.ac.ir/article_92366_a0dc48d4e2a979e7df4d895842b357b0.pdf |
| فایل مقاله |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
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