2. "Since the earliest
time, finance has
always been a
cornerstone of
human culture"
Simon wentch
From the days of barter to today’s
cryptocurrencies, finance has always been
associated with the generation of data, such as
banking transactions, credit, insurance, and
investment reports
Day-to-day operations in finance entail
producing and consuming large amounts of
unstructured text data from various sources.
3. However, the manual approaches to data
processing have over time been reduced in
use and importance
Because of this text analysis, the demand has increased significantly in recent years.
The field of text mining is constantly evolving alongside artificial intelligence. The
analysis of large numbers of financial data is both a requirement and an advantage
for companies, governments, and the general public.
Nowadays people predict and manage risks by text analysis, by making decisions
based on factual data and keep their customers happy and overcome their
competitors.
9. 1. Analysis can never achieve full accuracy due to the
involvement of confidential data
2. Text analysis models lack a well-defined understanding
of financial jargon.
3. Financial data is highly unstructured and redundant in
nature.
4. There are no dynamic text analysis models designed
specifically for financial operations.
12. Sentiment
Analysis
Analyze feedback from your
customers extracted from multiple
sources and identify the sentiments
of the market towards a brand
market reputation. This helps in the
prediction of stock market trends.