Authors :
Ashwini K. Bhavsar; Tejaswini H. Gavhane
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
http://tinyurl.com/2jbj9ufe
Scribd :
http://tinyurl.com/ys38j7kt
DOI :
https://doi.org/10.5281/zenodo.10643367
Abstract :
Moving ahead in this era of data, there is a lot
of information, which if used in the right way, can be
used in the financial domain as well, to determine the
market. This prediction can lead to large profits and
help in understanding the complex financial markets.
Sentiment analysis is a kind of data mining technique,
which can be used to process and understand the textual
content to derive meaningful insights. In this paper, for
the purpose of sentiment analysis, natural language
processing will be used, which is the area of machine
learning in the rise. The techniques will be applied here
onto a large dataset from Twitter and hence, analyse the
public opinions about the financial markets.
Keywords :
Predicting finances, Natural Language Processing(NLP),Financial Markets, Analysis of Sentiments, Mining Text.
Moving ahead in this era of data, there is a lot
of information, which if used in the right way, can be
used in the financial domain as well, to determine the
market. This prediction can lead to large profits and
help in understanding the complex financial markets.
Sentiment analysis is a kind of data mining technique,
which can be used to process and understand the textual
content to derive meaningful insights. In this paper, for
the purpose of sentiment analysis, natural language
processing will be used, which is the area of machine
learning in the rise. The techniques will be applied here
onto a large dataset from Twitter and hence, analyse the
public opinions about the financial markets.
Keywords :
Predicting finances, Natural Language Processing(NLP),Financial Markets, Analysis of Sentiments, Mining Text.
