  A key aspect of word of mouth marketing are emotions. Emotions in texts help
propagating messages in conventional advertising. In word of mouth scenarios,
emotions help to engage consumers and incite to propagate the message further.
While the function of emotions in offline marketing in general and word of
mouth marketing in particular is rather well understood, online marketing can
only offer a limited view on the function of emotions. In this contribution we
seek to close this gap. We therefore investigate how emotions function in
social media. To do so, we collected more than 30,000 brand marketing messages
from the Google+ social networking site. Using state of the art computational
linguistics classifiers, we compute the sentiment of these messages. Starting
out with Poisson regression-based baseline models, we seek to replicate earlier
findings using this large data set. We extend upon earlier research by
computing multi-level mixed effects models that compare the function of
emotions across different industries. We find that while the well known notion
of activating emotions propagating messages holds in general for our data as
well. But there are significant differences between the observed industries.
