  Sentiment analysis gets increasing attention in software engineering with new
tools emerging from new insights provided by researchers. Existing use cases
and tools are meant to be used for textual communication such as comments on
collaborative version control systems. While this can already provide useful
feedback for development teams, a lot of communication takes place in meetings
and is not suited for present tool designs and concepts.
  In this paper, we present a concept that is capable of processing live
meeting audio and classifying transcribed statements into sentiment polarity
classes. We combine the latest advances in open source speech recognition with
previous research in sentiment analysis. We tested our approach on a student
software project meeting to gain proof of concept, showing moderate agreement
between the classifications of our tool and a human observer on the meeting
audio. Despite the preliminary character of our study, we see promising results
motivating future research in sentiment analysis on meetings. For example, the
polarity classification can be extended to detect destructive behaviour that
can endanger project success.
