  Financial disclosure analysis and Knowledge extraction is an important
financial analysis problem. Prevailing methods depend predominantly on
quantitative ratios and techniques, which suffer from limitations like window
dressing and past focus. Most of the information in a firm's financial
disclosures is in unstructured text and contains valuable information about its
health. Humans and machines fail to analyze it satisfactorily due to the
enormous volume and unstructured nature, respectively. Researchers have started
analyzing text content in disclosures recently. This paper covers the previous
work in unstructured data analysis in Finance and Accounting. It also explores
the state of art methods in computational linguistics and reviews the current
methodologies in Natural Language Processing (NLP). Specifically, it focuses on
research related to text source, linguistic attributes, firm attributes, and
mathematical models employed in the text analysis approach. This work
contributes to disclosure analysis methods by highlighting the limitations of
the current focus on sentiment metrics and highlighting broader future research
areas
