(untitled) (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Prediction and analysis of stock market data is paramount in today’s day and age. Since the economic interactions are too complex for shallow neural networks this paper implements Long Short Term Memory (LSTM) neural networks. LSTM is chosen as it helps to vectorise the data and thus give better predictions. We’ve also utilised other algorithms to show the effectivity of each of these algorithms pitted against LSTM. A very important factor to consider while predicting the stock market is the mood of the people. A person’s emotions have the power to influence the stock market. Sentiment analysis on twitter is used to identify a correlation amongst the future of the stock and the general public’s mood. Our paper works on comparing the sentiment analysis and the predicted stock value and showing that the two are rather similar and that people’s emotions affects the future of the stock prices and to do a comparative study between prediction with and without using the results of the sentiment analysis.