From Textual to Verbal Communication: Towards Applying Sentiment Analysis to a Software Project Meeting
This addresses the gap in sentiment analysis tools for verbal communication in software engineering meetings, though it is incremental as it builds on existing methods.
The paper tackled the problem of applying sentiment analysis to live meeting audio in software projects, presenting a concept that combines speech recognition with sentiment analysis and showing moderate agreement with human classifications in a student project meeting.
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.