Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis

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Ayman E. Khedr 1,* S.E.Salama 2,3 Nagwa Yaseen 2

1. Faculty of Computers and Information Technology, Future University in Egypt, Information Systems Department, Cairo, Egypt

2. Faculty of Computers and Information, Helwan University, Information Systems Department Cairo, Egypt

3. Faculty of Computers and Information Technology, King Abduaziz University, Jeddah, Kingdom of Saudi

* Corresponding author.


Received: 20 Nov. 2016 / Revised: 19 Feb. 2017 / Accepted: 20 Apr. 2017 / Published: 8 Jul. 2017

Index Terms

Data Mining, Stock Market, sentiment analysis, Text Mining, news sentiment analysis


Stock market prediction has become an attractive investigation topic due to its important role in economy and beneficial offers. There is an imminent need to uncover the stock market future behavior in order to avoid investment risks. The large amount of data generated by the stock market is considered a treasure of knowledge for investors. This study aims at constructing an effective model to predict stock market future trends with small error ratio and improve the accuracy of prediction. This prediction model is based on sentiment analysis of financial news and historical stock market prices. This model provides better accuracy results than all previous studies by considering multiple types of news related to market and company with historical stock prices. A dataset containing stock prices from three companies is used. The first step is to analyze news sentiment to get the text polarity using naïve Bayes algorithm. This step achieved prediction accuracy results ranging from 72.73% to 86.21%. The second step combines news polarities and historical stock prices together to predict future stock prices. This improved the prediction accuracy up to 89.80%.

Cite This Paper

Ayman E. Khedr, S.E.Salama, Nagwa Yaseen,"Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.7, pp.22-30, 2017. DOI:10.5815/ijisa.2017.07.03


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