  This discussion paper demonstrates how longitudinal sentiment analyses can
depict intertemporal dynamics on social media platforms, what challenges are
inherent and how further research could benefit from a longitudinal
perspective. Furthermore and since tools for sentiment analyses shall simplify
and accelerate the analytical process regarding qualitative data at acceptable
inter-rater reliability, their applicability in the context of radicalization
research will be examined regarding the Tweets collected on January 6th 2021,
the day of the storming of the U.S. Capitol in Washington. Therefore, a total
of 49,350 Tweets will be analyzed evenly distributed within three different
sequences: before, during and after the U.S. Capitol in Washington was stormed.
These sequences highlight the intertemporal dynamics within comments on social
media platforms as well as the possible benefits of a longitudinal perspective
when using conditional means and conditional variances. Limitations regarding
the identification of supporters of such events and associated hate speech as
well as common application errors will be demonstrated as well. As a result,
only under certain conditions a longitudinal sentiment analysis can increase
the accuracy of evidence based predictions in the context of radicalization
research.
