We show that information about social relationships can be used to improve
user-level sentiment analysis. The main motivation behind our approach is that
users that are somehow "connected" may be more likely to hold similar opinions;
therefore, relationship information can complement what we can extract about a
user's viewpoints from their utterances. Employing Twitter as a source for our
experimental data, and working within a semi-supervised framework, we propose
models that are induced either from the Twitter follower/followee network or
from the network in Twitter formed by users referring to each other using "@"
mentions. Our transductive learning results reveal that incorporating
social-network information can indeed lead to statistically significant
sentiment-classification improvements over the performance of an approach based
on Support Vector Machines having access only to textual features.