Social media, such as Twitter, have been exploited by trolls to manipulate political discourse and spread disinformation during the 2016 US Presidential Election. Trolls are users of social media accounts created with intentions to influence the public opinion by posting or reposting messages containing misleading or inflammatory information with malicious intentions. There has been previous research that focused on troll detection using Machine Learning approaches, and troll understanding using visualizations, such as word clouds. In this paper, we focus on the content analysis of troll tweets to identify the major entities mentioned and the relationships among these entities, to understand the events and statements mentioned in Russian Troll tweets coming from the Internet Research Agency (IRA), a troll factory allegedly financed by the Russian government. We applied several NLP techniques to develop Knowledge Graphs to understand the relationships of entities, often mentioned by dispersed trolls, and thus hard to uncover. This integrated KG helped to understand the substance of Russian Trolls' influence in the election. We identified three clusters of troll tweet content: one consisted of information supporting Donald Trump, the second for exposing and attacking Hillary Clinton and her family, and the third for spreading other inflammatory content. We present the observed sentiment polarization using sentiment analysis for each cluster and derive the concern index for each cluster, which shows a measurable difference between the presidential candidates that seems to have been reflected in the election results.