Stock trend prediction relying on text mining and sentiment analysis with tweets

Abstract

Stock trend prediction based on text has gained much attention from researchers in recent years. According to investment theories, investors' behaviors will influence the stock market, and the way people invest their money is based on the history trend and information they hold. On account of this indirectly influential relationship between information of stock and stock trend, stock trend prediction based on text has been done by many researchers. However, due to the serious feature sparse problem in tweets and unreliability of using average sentiment score to indicate one day's sentiment, this work proposed a text-sentiment based stock trend prediction model with a hybrid feature selection method. Instead of applying sentiment analysis to add sentiment related features, this paper uses SentiWordNet to give an additional weight to the selected features. Besides, this work also compares the results with those of other learning algorithms. SVM linear algorithm based on leave-one-out cross validation yields the best performance of 90.34%.