  The impact of social media on the modern world is difficult to overstate.
Virtually all companies and public figures have social media accounts on
popular platforms such as Twitter and Facebook. In China, the micro-blogging
service provider, Sina Weibo, is the most popular such service. To influence
public opinion, Weibo trolls -- the so called Water Army -- can be hired to
post deceptive comments. In this paper, we focus on troll detection via
sentiment analysis and other user activity data on the Sina Weibo platform. We
implement techniques for Chinese sentence segmentation, word embedding, and
sentiment score calculation. In recent years, troll detection and sentiment
analysis have been studied, but we are not aware of previous research that
considers troll detection based on sentiment analysis. We employ the resulting
techniques to develop and test a sentiment analysis approach for troll
detection, based on a variety of machine learning strategies. Experimental
results are generated and analyzed. A Chrome extension is presented that
implements our proposed technique, which enables real-time troll detection when
a user browses Sina Weibo.
