期刊名称：International Journal of Computer Science & Information Technology (IJCSIT)
印刷版ISSN：0975-4660
电子版ISSN：0975-3826
出版年度：2016
卷号：8
期号：3
页码：67
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much researchhas been carried in the area of prediction of stocks. This project is about taking non quantifiable data suchas financial news articles about a company and predicting its future stock trend with news sentimentclassification. Assuming that news articles have impact on stock market, this is an attempt to studyrelationship between news and stock trend. To show this, we created three different classification modelswhich depict polarity of news articles being positive or negative. Observations show that RF and SVMperform 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 areobtained in all of the experiments. The accuracy of the prediction model is more than 80% and incomparison with news random labelling with 50% of accuracy; the model has increased the accuracy by30%
