SigmaLaw-ABSA: Dataset for Aspect-Based Sentiment Analysis in Legal Opinion Texts
This addresses a gap for researchers in legal NLP by providing a new dataset, but it is incremental as it applies existing ABSA methods to a new domain.
The authors tackled the lack of a publicly available dataset for Aspect-Based Sentiment Analysis (ABSA) in legal opinion texts by introducing SigmaLaw-ABSA, a manually annotated dataset in English, and provided baseline performance results from existing deep learning systems.
Aspect-Based Sentiment Analysis (ABSA) has been prominent and ongoing research over many different domains, but it is not widely discussed in the legal domain. A number of publicly available datasets for a wide range of domains usually fulfill the needs of researchers to perform their studies in the field of ABSA. To the best of our knowledge, there is no publicly available dataset for the Aspect (Party) Based Sentiment Analysis for legal opinion texts. Therefore, creating a publicly available dataset for the research of ABSA for the legal domain can be considered as a task with significant importance. In this study, we introduce a manually annotated legal opinion text dataset (SigmaLaw-ABSA) intended towards facilitating researchers for ABSA tasks in the legal domain. SigmaLaw-ABSA consists of legal opinion texts in the English language which have been annotated by human judges. This study discusses the sub-tasks of ABSA relevant to the legal domain and how to use the dataset to perform them. This paper also describes the statistics of the dataset and as a baseline, we present some results on the performance of some existing deep learning based systems on the SigmaLaw-ABSA dataset.