  Social aspects in software development teams are of particular importance for
a successful project closure. To analyze sentiments in software projects, there
are several tools and approaches available. These tools analyze text-based
communication based on the used words to predict whether they appear to be
positive, negative, or neutral for the receiver of the message. In the research
project ComContA, we investigate so-called sentiment analysis striving to
analyze the content of text-based communication in development teams with
regard to the statement's polarity. That is, we analyze whether the
communication appears to be adequate (i.e., positive or neutral) or negative.
In a workshop paper, we presented a tool called SEnti-Analyzer that allows to
apply sentiment analysis to verbal communication in meetings of software
projects. In this technical report, we present the extended functionalities of
the SEnti-Analyzer by also allowing the analysis of text-based communication,
we improve the prediction of the tool by including established sentiment
analysis tools, and we evaluate the tool with respect to its accuracy. We
evaluate the tool by comparing the prediction of the SEnti-Analyzer to
pre-labeled established data sets used for sentiment analysis in software
engineering and to perceptions of computer scientists. Our results indicate
that in almost all cases at least two of the three votes coincide, but in only
about half of the cases all three votes coincide. Our results raise the
question of the "ultimate truth" of sentiment analysis outcomes: What do we
want to predict with sentiment analysis tools? The pre-defined labels of
established data sets? The perception of computer scientists? Or the perception
of single computer scientists which appears to be the most meaningful
objective?
