Title: Studying Positive Speech on Twitter,
Abstract: We present results of empirical studies on positive speech on Twitter. By
positive speech we understand speech that works for the betterment of a given
situation, in this case relations between different communities in a
conflict-prone country. We worked with four Twitter data sets. Through
semi-manual opinion mining, we found that positive speech accounted for < 1% of
the data . In fully automated studies, we tested two approaches: unsupervised
statistical analysis, and supervised text classification based on distributed
word representation. We discuss benefits and challenges of those approaches and
report empirical evidence obtained in the study.