Prediction of stock market price movements based on sentiment analysis on various news headlines

Abstract

Due to the recent advances in social media, stock-related news is available on every social media platform, imposing a value to improve stock price prediction performance. We used linear discriminant analysis (LDA) to screen chip indicators, and we build a basic stock prediction model. We have gathered stock-related news headlines and then quantify the unstructured data into sentiment scores using text mining technology which can be correlated with the stock price changes. We have proposed an augmented prediction model, and our results suggest that prediction accuracy for stock prices can be improved, and it will be advantageous for stock investors regarding their investment strategies. In this research, we have analysed more than 49,000 news headlines to determine the relationship between the news headlines and stock price on a given date. Using novel data visualisation and natural language processing (NLP) techniques, we have implemented data visualisations showing how the company share prices are affected based on the sentiment scores we are getting from analysing the news headlines.