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Authors
Affiliations
1 Department of Computer Engineering, K. J. Somaiya College of Engineering, Mumbai, IN
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 8, No 3 (2016), Pagination: 67-76
Abstract
Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much research has been carried in the area of prediction of stocks. This project is about taking non quantifiable data such as financial news articles about a company and predicting its future stock trend with news sentiment classification. Assuming that news articles have impact on stock market, this is an attempt to study relationship between news and stock trend. To show this, we created three different classification models which depict polarity of news articles being positive or negative. Observations show that RF and SVM perform well in all types of testing. Naïve Bayes gives good result but not compared to the other two. Experiments are conducted to evaluate various aspects of the proposed model and encouraging results are obtained in all of the experiments. The accuracy of the prediction model is more than 80% and in comparison with news random labelling with 50% of accuracy; the model has increased the accuracy by 30%.
Keywords
Text Mining, Sentiment Analysis, Naive Bayes, Random forest, SVM, Stock Trends.
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