Negation scope detection in sentiment analysis: Decision support for news-driven trading

Abstract

Decision support for financial news using natural language processing requires robust methods that process all sentences correctly, including those that are negated. To predict the corresponding negation scope, related literature commonly utilizes rule-based algorithms and generative probabilistic models. In contrast, we propose the use of a tailored reinforcement learning method, since it can conquer learning task of arbitrary length. We then perform a thorough comparison with a two-pronged evaluation. First, we compare the predictive performance using a manually-labeled dataset. Here, reinforcement learning outperforms common approaches from the related literature, leading to a balanced classification accuracy of up to 70.17%. Second, we examine how detecting negation scopes can improve the accuracy of sentiment analysis for financial news, leading to an improvement of up to 10.63% in the correlation between news sentiment and stock market returns. This reveals negation scope detection as a crucial leverage in decision support from sentiment.