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.
@article{arxiv.1702.08866,
title = {Studying Positive Speech on Twitter},
author = {Marina Sokolova and Vera Sazonova and Kanyi Huang and Rudraneel Chakraboty and Stan Matwin},
journal= {arXiv preprint arXiv:1702.08866},
year = {2017}
}