Title :
Sentiment Analysis on News Articles for Stocks
Author :
Kalyanaraman, Vaanchitha ; Kazi, Sarah ; Tondulkar, Rohan ; Oswal, Sangeeta
Author_Institution :
VESIT, Mumbai, India
Abstract :
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.
Keywords :
application program interfaces; learning (artificial intelligence); stock markets; text analysis; Bing API; machine learning algorithms; news articles; sentiment analysis; sentiment dictionary; stock articles; stock prices; Accuracy; Companies; Dictionaries; Linear regression; Machine learning algorithms; Training; Vectors; Machine Learning; Sentiment; Sentiment;
Conference_Titel :
Modelling Symposium (AMS), 2014 8th Asia
Print_ISBN :
978-1-4799-6486-4
DOI :
10.1109/AMS.2014.14