Building a Sentiment Dictionary for News Analytics using Stock Prices

Abstract

This paper proposes a method of building a sentiment dictionary using only news and stock price data for textual analysis in finance. To obtain word polarity from stock price fluctuations, we calculate stock price returns following announcements of news articles. We constructed learners with support vector regression, using stock price returns as supervised labels of news articles, and built a sentiment dictionary by extracting word polarity from learners. Furthermore, we examined whether our sentiment dictionary is effective in classifying news articles as negative or positive. We found that our sentiment dictionary is also effective in classifying news articles provided by other news media other than news media we employed in constructing the algorithm. In addition, we found that it is difficult to classify news articles on a date that is two trading days away from the news announcement date.