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.