  Increasing popularity of Twitter in politics is subject to commercial and
academic interest. To fully exploit the merits of this platform, reaching the
target audience with desired political leanings is critical. This paper extends
the research on inferring political orientations of Twitter users to the case
of 2017 Turkish constitutional referendum. After constructing a targeted
dataset of tweets, we explore several types of potential features to build
accurate machine learning based predictive models. In our experiments, a
three-class support vector machine (SVM) classifier trained on semantic
features achieves the best accuracy score of 89.9%. Moreover, an SVM classifier
trained on full-text features performs better than an SVM classifier trained on
hashtags, with respective accuracy scores of 89.05% and 85.9%. Relatively high
accuracy scores obtained by full-text features may point to differences in
language use, which deserves further research.
