A two-phase sentiment analysis approach for judgement prediction

Yi Hung Liu, Yen Liang Chen

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

Factual scenario analysis of a judgement is critical to judges during sentencing. With the increasing number of legal cases, professionals typically endure heavy workloads on a daily basis. Although a few previous studies have applied information technology to legal cases, according to our research, no prior studies have predicted a pending judgement using legal documents. In this article, we introduce an innovative solution to predict relevant rulings. The proposed approach employs text mining methods to extract features from precedents and applies a text classifier to automatically classify judgements according to sentiment analysis. This approach can assist legal experts or litigants in predicting possible judgements. Experimental results from a judgement data set reveal that our approach is a satisfactory method for judgement classification.

Original languageEnglish
Pages (from-to)594-607
Number of pages14
JournalJournal of Information Science
Volume44
Issue number5
DOIs
StatePublished - 1 Oct 2018

Keywords

  • Criminal precedent
  • judgement classification
  • sentiment analysis
  • support vector machines (SVM)
  • text mining

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