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

Analyzing Mass Media influence using natural language processing and time series analysis