ScienceDirect Available online at www.sciencedirect.com Procedia Computer Science 218 (2023) 1067–1078 1877-0509 © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientifc committee of the International Conference on Machine Learning and Data Engineering 10.1016/j.procs.2023.01.086 © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientifc committee of the International Conference on Machine Learning and Data Engineering Keywords:Financial news; Forecasting; MLP Regressor; News sentiment analysis; Stock Market; Stock prediction 1. Introduction STOCK prediction has always been one of the challenging problems of economists, statisticians, and other financial experts as it is a vital component of a country’s economy. The stock market is an area where stocks can be traded, transferred, and distributed. The stock market gives companies a chance of expanding and raising money through Initial Public Offerings (IPO) [1]. Investors can invest in stocks of various companies and can make money if they will be able to decide when to buy and when sell particular stocks. The stock market is very volatile as the prices of stocks of particular companies are dynamic and keep changing depending on the volume of shares bought and sold * Dr Preeti Aggarwal. Tel.: +91-98721021863. E-mail address:pree_agg@pu.ac.in International Conference on Machine Learning and Data Engineering Stock Prediction by Integrating Sentiment Scores of Financial News and MLP-Regressor: A Machine Learning Approach Junaid Maqbool a , Preeti Aggarwal a *, Ravreet Kaur a , Ajay Mittal a , Ishfaq Ali Ganaie b a Department of Computer Science and Engineering, UIET, Panjab University, Sector 25, Chandigarh, 160014, India, b Panjab University, Chandigarh Abstract The stock market is highly volatile as it depends on political, financial, environmental, and various internal and external factors along with historical stock data. Such information is available to people through microblogs and news and predicting stock price merely on historical data is hard. The high volatility emphasizes the importance to check the effect of external factors on the stock market. In this paper, a machine learning model is proposed where the financial news is used along with historical stock price data to predict upcoming stock prices. The paper has used three algorithms to calculate various sentiment scores and used them in different combinations to understand the impact of financial news on stock price as well the impact of each sentiment scoring algorithm. Experiments have been conducted on ten-year historical stock price data as well as financial news of four different companies from different sectors to predict the next day and next week’s stock trends and accuracy metrics were checked for a period of 10, 30, and 100 days. The proposed model can achieve the highest accuracy of 0.90 for both trend and future trends for a period of 10 days. Experiments have also been performed to check the difficulty in predicting some stocks. It was found that Tata Motors an automobile company stock prediction has maximum MAPE and hence deviates more from actual prediction as compared to others.