Sentimental Analysis of News Headlines for Stock Market

Predicting the stock market is like one of the most common things one does. Predicting exact value is like a futile attempt but one thing is sure we can know the trend or so called the direction in which the price of the stock will move. The stock market is driven by a lot of factors and the majority of them are in the form of news articles. The impact of news on stocks is quite significant. The direction of stock price will be dictated by the sentiments of the market participants. The sentimental analysis is no new topic people have been doing it for quite a long time by implementing several models. In our research paper, we are comparing the results from different models under the same circumstances and concluding which one of the compared models is better based on their accuracy. The methods that we have used for comparison are K-Mean clustering, Naive Bayes, and Support Vector Machine. In our experimental study for sentimental analysis for news headlines, we found that Support Vector Machine and Naive Bayes have better accuracy than K-Means clustering.