With the advent of technology, it has become an area of interest to forecast
stock prices based on the news. Important “new news” is most likely to affect the stock
price. The growth of social media platforms has enabled users to express their views on
commonplace issues. Hence, the feedback from the public and customers is important.
Sentiment analysis, also known as opinion mining, is a popular pre-planning exercise
for conversations that seek to identify the underlying feelings associated with various
text types. Moreover, public opinion research can provide us useful information.
Sentiment analysis is a powerful tool with numerous applications. It can be used to
understand user attitudes on social media sites such as Facebook and Twitter.
This sentiment analysis helps determine the customer's inclination, which in turn
affects market trends. These trends often indicate a shift in stock prices in response to
major announcements and significant news. With the aid of machine learning
algorithms, suitably training and testing the data, this effect has been studied, and
significant price movements have been observed. This paper employs sentiment
analysis, machine learning, and deep learning techniques to examine the influence of
news on market prediction, diverging from traditional stock structured data analysis.
However, the efficacy and accuracy of sentiment analysis are being hampered by issues
with Natural Language Processing (NLP). Recent studies have shown that deep
learning models offer a promising approach to addressing the challenges in natural
language processing.
Keywords: Deep learning, Machine learning, Natural language processing, Neural network, News-based stock price prediction, Sentiment analysis.
