Integration of Machine Learning Techniques for Effective Sentiment Analysis on Stock Market News

Sakshi Borah, Shallu Bashambu

Vol. 6, Jan-Dec 2020

Page Number: 061-066

Abstract:

Stock market forecasting is very important in the planning of business activities. Stock price prediction has attracted many researchers in multiple disciplines including computer science, statistics, economics, finance, and operations research. Recent studies have shown that the vast amount of online information in the public domain such as Wikipedia usage pattern, news stories from the mainstream media, and social media discussions can have an observable effect on investors’ opinions towards financial markets. The reliability of the computational models on stock market prediction is important as it is very sensitive to the economy and can directly lead to financial loss. In this paper, we retrieved, extracted, and analysed the effects of news sentiments on the stock market. Our main contributions include the development of a dictionary-based sentiment analysis model and the evaluation of the model for gauging the effects of news sentiments on the stocks. Using only news sentiments, we achieved a directional accuracy of ~97% in predicting the trends in short-term stock price movement.

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