(untitled) (International Journal of Innovative Research in Computer Science & Technology, 2021)
Stock price prediction is a trending concepts in today’s world. Proposed work use Twitter data to predict public mood and use the predicted mood to predict the stock market movements. The ceaseless use of social media in the contemporary era has reached unprecedented levels, which has led to the belief that the expressed public sentiment could be correlated with the behaviour of stock prices. Here we develop a system which collects past tweets, processes them further, and examines the electiveness of various machine learning techniques such as Naive Bayes classification and XgBoost algorithm, for providing a positive, negative or neutral sentiment on the tweet corpus. Subsequently, work employ an equivalent machine learning algorithms to analyse how tweets correlate with stock market price behaviour. Finally, examine our prediction’s error by comparing our algorithm’s outcome with next day’s actual close price. Here proposed work takes data from Twitter and also to improve the accuracy proposed work also takes stock data from newspapers and yahoo finance also. The final results seem to be promising as we found correlation between sentiment of tweets and stock prices.