  The ability to change arbitrary aspects of a text while leaving the core
message intact could have a strong impact in fields like marketing and politics
by enabling e.g. automatic optimization of message impact and personalized
language adapted to the receiver's profile. In this paper we take a first step
towards such a system by presenting an algorithm that can manipulate the
sentiment of a text while preserving its semantics using disentangled
representations. Validation is performed by examining trajectories in embedding
space and analyzing transformed sentences for semantic preservation while
expression of desired sentiment shift.
