  A key question of collective social behavior is related to the influence of
Mass Media on public opinion. Different approaches have been developed to
address quantitatively this issue, ranging from field experiments to
mathematical models. In this work we propose a combination of tools involving
natural language processing and time series analysis. We compare selected
features of mass media news articles with measurable manifestation of public
opinion. We apply our analysis to news articles belonging to the 2016 U.S.
presidential campaign. We compare variations in polls (as a proxy of public
opinion) with changes in the connotation of the news (sentiment) or in the
agenda (topics) of a selected group of media outlets. Our results suggest that
the sentiment content by itself is not enough to understand the differences in
polls, but the combination of topics coverage and sentiment content provides an
useful insight of the context in which public opinion varies. The methodology
employed in this work is far general and can be easily extended to other topics
of interest.
