A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
This paper reports on an evaluation of five commonly used, lexicon-based sentiment analysis tools (Mean-ingCloud, ParallelDots, Repustate, RSentiment for R, SentiStrength), tested for accuracy against a collection of Trump's tweets spanning from election day November 2016 to one year post inauguration (January 2018). Repustate was found to be the most accurate at 67.53%. Our preliminary analysis suggests that this percentage reflects Trump's frequent inclusion of both positive and negativedoi:10.5220/0007759306440651 dblp:conf/iceis/PerryNN19 fatcat:zycw76vi3fcvpndirbv7m5turu