SOPS: Stock Prediction Using Web Sentiment

Abstract

Recently, the web has rapidly emerged as a great source of financial information ranging from news articles to per- sonal opinions. Data mining and analysis of such financial information can aid stock market predictions. Traditional approaches have usually relied on predictions based on past performance of the stocks. In this paper, we introduce a novel way to do stock market prediction based on sentiments of web users. Our method involves scanning for financial message boards and extracting sentiments expressed by in- dividual authors. The system then learns the correlation between the sentiments and the stock values. The learned model can then be used to make future predictions about stock values. In our experiments, we show that our method is able to predict the sentiment with high precision and we also show that the stock performance and its recent web sentiments are also closely correlated.

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