Sentiment Analysis on News Articles for Stocks

In this paper we have used sentiment analysis on news articles to see its effect on stock prices. We collected our dataset using Bing API which gave us links to news articles about a specific company. As no pre-existing sentiment dictionary specifically for stock articles exited, we created a specialized sentiment dictionary only meant to analyze stock articles. Two different machine learning algorithms were applied to the dataset and the accuracy of the two was compared. In order to test our results we attached an overall sentiment to each article in our data set which was compared to the predicted sentiment by the algorithm. We also compared the predicted results with the actual change in the stock prices on the market.

I. Introduction

Analyzing stock trends has always been an interesting area of research, mainly because no universal rule to predict change in stock prices has been discovered. More research began with most stock exchanges becoming partially or fully electronic. By introducing the electronic element, stock traders and research analysts were able to obtain more accurate data of the movement of stock prices. This has made analyzing the market all the more interesting as in the financial sector where abundance of financial information is present; we can analyze and present it to the investor.