  An expanding field of substantive interest for the theory of the law and the
practice-of-law entails Legal Sentiment Analysis and Opinion Mining (LSAOM),
consisting of two often intertwined phenomena and actions underlying legal
discussions and narratives: (1) Sentiment Analysis (SA) for the detection of
expressed or implied sentiment about a legal matter within the context of a
legal milieu, and (2) Opinion Mining (OM) for the identification and
illumination of explicit or implicit opinion accompaniments immersed within
legal discourse. Efforts to undertake LSAOM have historically been performed by
human hand and cognition, and only thinly aided in more recent times by the use
of computer-based approaches. Advances in Artificial Intelligence (AI)
involving especially Natural Language Processing (NLP) and Machine Learning
(ML) are increasingly bolstering how automation can systematically perform
either or both of Sentiment Analysis and Opinion Mining, all of which is being
inexorably carried over into engagement within a legal context for improving
LSAOM capabilities. This research paper examines the evolving infusion of AI
into Legal Sentiment Analysis and Opinion Mining and proposes an alignment with
the Levels of Autonomy (LoA) of AI Legal Reasoning (AILR), plus provides
additional insights regarding AI LSAOM in its mechanizations and potential
impact to the study of law and the practicing of law.
