  Analysis and extraction of useful information from legal judgments using
computational linguistics was one of the earliest problems posed in the domain
of information retrieval. Presently, several commercial vendors exist who
automate such tasks. However, a crucial bottleneck arises in the form of
exorbitant pricing and lack of resources available in analysis of judgements
mete out by Hong Kong's Legal System. This paper attempts to bridge this gap by
providing several statistical, machine learning, deep learning and zero-shot
learning based methods to effectively analyze legal judgments from Hong Kong's
Court System. The methods proposed consists of: (1) Citation Network Graph
Generation, (2) PageRank Algorithm, (3) Keyword Analysis and Summarization, (4)
Sentiment Polarity, and (5) Paragrah Classification, in order to be able to
extract key insights from individual as well a group of judgments together.
This would make the overall analysis of judgments in Hong Kong less tedious and
more automated in order to extract insights quickly using fast inferencing. We
also provide an analysis of our results by benchmarking our results using Large
Language Models making robust use of the HuggingFace ecosystem.
