Constructing Financial Sentiment Lexicons for Individual Stocks

Abstract

This paper reports on our ongoing work to construct sentiment lexicons in the financial domain. Our approach takes advantages of news headlines and a given financial variable, such as stock prices, so as to generate initial sentiment lexicons. The initial lexicons are then filtered based on their co-occurrences with financial seed words and are subsequently expanded by analogical reasoning by using distributed representation of words. Evaluative experiments on around 12 years' worth of news data show that the resulting lexicons are mostly reasonable. As a possible application of the lexicons, trading simulation is also carried out, showing promising results.