Detection of Temporality at Discourse Level on Financial News by
  Combining Natural Language Processing and Machine Learning
Silvia García-Méndez Francisco de Arriba-Pérez Ana Barros-Vila Francisco J. González-Castaño
Finance-related news such as Bloomberg News, CNN Business and Forbes are
valuable sources of real data for market screening systems. In news, an expert
shares opinions beyond plain technical analyses that include context such as
political, sociological and cultural factors. In the same text, the expert
often discusses the performance of different assets. Some key statements are
mere descriptions of past events while others are predictions. Therefore,
understanding the temporality of the key statements in a text is essential to
separate context information from valuable predictions. We propose a novel
system to detect the temporality of finance-related news at discourse level
that combines Natural Language Processing and Machine Learning techniques, and
exploits sophisticated features such as syntactic and semantic dependencies.
More specifically, we seek to extract the dominant tenses of the main
statements, which may be either explicit or implicit. We have tested our system
on a labelled dataset of finance-related news annotated by researchers with
knowledge in the field. Experimental results reveal a high detection precision
compared to an alternative rule-based baseline approach. Ultimately, this
research contributes to the state-of-the-art of market screening by identifying
predictive knowledge for financial decision making.