  The automatic content analysis of mass media in the social sciences has
become necessary and possible with the raise of social media and computational
power. One particularly promising avenue of research concerns the use of
sentiment analysis in microblog streams. However, one of the main challenges
consists in aggregating sentiment polarity in a timely fashion that can be fed
to the prediction method. We investigated a large set of sentiment aggregate
functions and performed a regression analysis using political opinion polls as
gold standard. Our dataset contains nearly 233 000 tweets, classified according
to their polarity (positive, negative or neutral), regarding the five main
Portuguese political leaders during the Portuguese bailout (2011-2014). Results
show that different sentiment aggregate functions exhibit different feature
importance over time while the error keeps almost unchanged.
