  Software development encompasses many collaborative tasks in which usually
several persons are involved. Close collaboration and the synchronization of
different members of the development team require effective communication. One
established communication channel are meetings which are, however, often not as
effective as expected. Several approaches already focused on the analysis of
meetings to determine the reasons for inefficiency and dissatisfying meeting
outcomes. In addition to meetings, text-based communication channels such as
chats and e-mails are frequently used in development teams. Communication via
these channels requires a similar appropriate behavior as in meetings to
achieve a satisfying and expedient collaboration. However, these channels have
not yet been extensively examined in research. In this paper, we present an
approach for analyzing interpersonal behavior in text-based communication
concerning the conversational tone, the familiarity of sender and receiver, the
sender's emotionality, and the appropriateness of the used language. We
evaluate our approach in an industrial case study based on 1947 messages sent
in a group chat in Zulip over 5.5 months. Using our approach, it was possible
to automatically classify written sentences as positive, neutral, or negative
with an average accuracy of 62.97% compared to human ratings. Despite this
coarse-grained classification, it is possible to gain an overall picture of the
adequacy of the textual communication and tendencies in the group mood.
