Abstract:
Sentiment Analysis can be considered as an integral part of Natural Language
Processing with a wide variety of significant use cases related to different application
domains. Analyzing sentiments of descriptions that are given in Legal
Opinion Texts has the potential to be applied in several legal information extraction
tasks such as predicting the judgement of a legal case, predicting the winning
party of a legal case, and identifying contradictory opinions and statements. However,
the lack of annotated datasets for legal sentiment analysis imposes a major
challenge when developing automatic approaches for legal sentiment analysis using
supervised learning. In this work, we demonstrate an effective approach to
develop reliable sentiment annotators for legal domain while utilizing a minimum
number of resources. In that regard, we made use of domain adaptation techniques
based on transfer learning, where a dataset from a high resource source
domain is adapted to the target domain (legal opinion text domain). In this work,
we have come up with a novel approach based on domain specific word representations
to minimize the drawbacks that can be caused due to the differences
in language semantics between the source and target domains when adapting a
dataset from a source domain to a target domain. This novel approach is based
on the observations that were derived using several word representational and
language modelling techniques that were trained using legal domain specific copora.
In order to evaluate different word representational techniques in the legal
domain, we have prepared a legal domain specific context based verb similarity
dataset named LeCoVe . The experiments carried out within this research work
demonstrate that our approach to develop sentiment annotators for legal domain
in a low resource setting is successful with promising results and significant improvements
over existing works.
Citation:
Ratnayaka, G. (2022). Minimizing domain bias when adapting sentiment analysis techniques to the legal domain [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21669