Abstract:
Machine learning and deep learning are becoming more and more effective
techniques for evaluating financial data, encompassing textual, statistical, and digital
information. Future stock prediction is a prominent and challenging deep learning
topic in the industry. The difficulty in predicting future stock market stems from too
many diverse elements that simultaneously influence the amplitude and frequency of
stock market rise and falls. In this research work, the main focus is on the problem of
stock market trends predictions using social media as a tool. Digital networks are a
fast-growing area of information on the Internet. Perhaps one of the most important
features is the instant availability of more knowledge and the users' ability to converse
swiftly. Different Deep Learning algorithms (like CNN, RNN, GRU, and Bi-
Directional RNN) were used to forecast stock market trends based on information
from social media, as this data might influence investor behavior. Algorithms were
used to investigate the impact of social media accounts on stock market prediction
performance. The dataset chosen was an expert and public Twitter post from two
prominent technology firms, Alphabet Inc. (Google) and Apple Inc, and news data
related to these famous firms. The thesis employed deep learning methods, a pre-
trained language model for economic sentiment analysis, to extract sentiments from
tweets. With the help of this research, it will become easy for an investor to invest his
money in companies whose stock market values are high on the basis of sentiment
classification and will not lead them to any financial crises. SMP aims to anticipate
how the stock value of an economic trade will fluctuate in the foreseeable. If
shareholders can precisely estimate stock market progression, investors will indeed be
able to turn a profit. Finally, the study predicted the trends by modeling the Data on
the proposed GRU model, which outperforms the result of other algorithms. The GRU
model has shown significant results with an accuracy of 82.41%.
