SigmaLaw PBSA - A Deep Learning
Approach For Aspect Based Sentiment Analysis in Legal Opinion Texts
(pp101-115)
Isanka Rajapaksha,
Chanika Ruchini Mudalige,
Dilini Karunarathna,
Nisansa de
Silva, Gathika Ratnayaka,
and Amal Shehan Perera
doi:
https://doi.org/10.26421/JDI3.1-1
Abstracts:
When lawyers and legal
officers are working on a new legal case, they are supposed have
properly studied prior cases similar to the current case, as the
prior cases can provide valuable information which can have a direct
impact on the outcomes of the current court case. Therefore,
developing methodologies which are capable of automatically
extracting information from legal opinion texts related to previous
court cases can be considered as an important tool when it comes to
the legal technology ecosystem. In this study, we focus on finding
advantageous and disadvantageous facts or arguments in court cases,
which is one of the most critical and time-consuming tasks in court
case analysis. The Aspect-based Sentiment Analysis concept is used
as the base of this study to perform legal information extraction.
In this paper, we introduce a solution to predict sentiment value of
sentences in legal documents in relation to its legal parties. The
proposed approach employs a fine-grained sentiment analysis
(Aspect-Based Sentiment Analysis) technique to achieve this task.
Sigmalaw
PBSA
is a novel deep learning-based model for
ABSA
which is specifically designed for legal opinion texts. We evaluate
the
Sigmalaw
PBSA
model and existing
ABSA
models on the
SigmaLaw-ABSA
dataset
which consists of 2000 legal opinion texts fetched from a public
online data base. Experiments show that our model outperforms the
state-of-the-art models. We also conduct an ablation study to
identify which methods are most effective for legal texts.
Key words:
Legal Information
Extraction, Legal domain, Aspect-Based Sentiment Analysis, Deep
learning, NLP