  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.
