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arXiv · 1702.08866

Studying Positive Speech on Twitter

Marina Sokolova·Vera Sazonova·Kanyi Huang·Rudraneel Chakraboty·Stan Matwin
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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.

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Marina Sokolova, Vera Sazonova, Kanyi Huang, Rudraneel Chakraboty, Stan Matwin. 2017-02-24. Studying Positive Speech on Twitter. https://arxiv.org/abs/1702.08866

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