Detection of financial opportunities in micro-blogging data with a
  stacked classification system
Francisco de Arriba-Pérez Silvia García-Méndez José A. Regueiro-Janeiro Francisco J. González-Castaño
Micro-blogging sources such as the Twitter social network provide valuable
real-time data for market prediction models. Investors' opinions in this
network follow the fluctuations of the stock markets and often include educated
speculations on market opportunities that may have impact on the actions of
other investors. In view of this, we propose a novel system to detect positive
predictions in tweets, a type of financial emotions which we term
"opportunities" that are akin to "anticipation" in Plutchik's theory.
Specifically, we seek a high detection precision to present a financial
operator a substantial amount of such tweets while differentiating them from
the rest of financial emotions in our system. We achieve it with a three-layer
stacked Machine Learning classification system with sophisticated features that
result from applying Natural Language Processing techniques to extract valuable
linguistic information. Experimental results on a dataset that has been
manually annotated with financial emotion and ticker occurrence tags
demonstrate that our system yields satisfactory and competitive performance in
financial opportunity detection, with precision values up to 83%. This
promising outcome endorses the usability of our system to support investors'
decision making.