  Information systems experience an ever-growing volume of unstructured data,
particularly in the form of textual materials. This represents a rich source of
information from which one can create value for people, organizations and
businesses. For instance, recommender systems can benefit from automatically
understanding preferences based on user reviews or social media. However, it is
difficult for computer programs to correctly infer meaning from narrative
content. One major challenge is negations that invert the interpretation of
words and sentences. As a remedy, this paper proposes a novel learning strategy
to detect negations: we apply reinforcement learning to find a policy that
replicates the human perception of negations based on an exogenous response,
such as a user rating for reviews. Our method yields several benefits, as it
eliminates the former need for expensive and subjective manual labeling in an
intermediate stage. Moreover, the inferred policy can be used to derive
statistical inferences and implications regarding how humans process and act on
negations.
