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Unified Legal Party Based Sentiment Analysis Pipeline

Sahan Jayasinghe, Lakith Ransika Rambukkanage, Ashan Silva, Nisansa de Silva, Amal Shehan Perera
Q4 Journal The International Journal on Advances in ICT for Emerging Regions

The rapid growth of text corpora across various domains has emerged a need and an opportunity to leverage Natural Language Processing to automate and efficiently streamline tedious manual tasks. Legal domain is one such text rich domain which suffers a rapid growth of text corpora and requirement for natural language processing applications. In the pursuit of automating the prediction of the winning party of a court case among other usages, analysing sentiment in a party wise manner is beneficial for legal professionals. The two main sub-tasks in this process is to identify parties in a court case and afterwards analysing the respective sentiment towards each party. In this study we discuss the unification of two such models capable of doing the two task into a single pipeline to perform party based sentiment analysis efficiently.

Keywords: Natural Language Processing | Machine Learning / Deep Learning | Law | Aspect-based Sentiment Analysis | Legal Party Identification | Sentiment Analysis | 

@article{jayasinghe2022unified,
  title={Unified Legal Party Based Sentiment Analysis Pipeline},
  author={Jayasinghe, Sahan and Rambukkanage, Lakith Ransika and Silva, Ashan and de Silva, Nisansa and Perera, Amal Shehan},
  journal={The International Journal on Advances in ICT for Emerging Regions},
  volume={15},
  number={2},
  year={2022},
  abstract={The rapid growth of text corpora across various domains has emerged a need and an opportunity to leverage Natural Language Processing to automate and efficiently streamline tedious manual tasks. Legal domain is one such text rich domain which suffers a rapid growth of text corpora and requirement for natural language processing applications. In the pursuit of automating the prediction of the winning party of a court case among other usages, analysing sentiment in a party wise manner is beneficial for legal professionals. The two main sub-tasks in this process is to identify parties in a court case and afterwards analysing the respective sentiment towards each party. In this study we discuss the unification of two such models capable of doing the two task into a single pipeline to perform party based sentiment analysis efficiently.},
  misc={https://journal.icter.org/index.php/ICTer/article/view/388/110,NLP:ML:LAW,https://goo.gl/iY6aTr},
  doi={10.4038/icter.v15i2.7247}
}

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PI: Nisansa de Silva

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