  We present work on sentiment analysis in Twitter for Macedonian. As this is
pioneering work for this combination of language and genre, we created suitable
resources for training and evaluating a system for sentiment analysis of
Macedonian tweets. In particular, we developed a corpus of tweets annotated
with tweet-level sentiment polarity (positive, negative, and neutral), as well
as with phrase-level sentiment, which we made freely available for research
purposes. We further bootstrapped several large-scale sentiment lexicons for
Macedonian, motivated by previous work for English. The impact of several
different pre-processing steps as well as of various features is shown in
experiments that represent the first attempt to build a system for sentiment
analysis in Twitter for the morphologically rich Macedonian language. Overall,
our experimental results show an F1-score of 92.16, which is very strong and is
on par with the best results for English, which were achieved in recent SemEval
competitions.
