Using Big Data Analytics for Sentiment Analysis to Explore Team Communication Dynamics in Human Machine Interactions for Team Situational Awareness

The team communication dynamics is one of the crucial factors that can provide a plethora of information regarding the evolution of situational awareness and in turn highlight the pivotal moments for mission success in any combat training simulation scenario. Specifically, in this work, we study the team communication dynamics data collected from a firefighting simulation. The large volume of communication between individual team members recorded throughout the simulation training provides an ideal basis for the use of Big Data analytics and sentiment analysis to derive deeper insights on various levels of situational awareness observed throughout the simulations. We are interested in understanding the relationship between specific cluster of words or sentiments that are associated with various levels of situational awareness. In particular, we explore the relationship between team communication frequency dynamics, and its relationship to team situational awareness and team performance in a fire-fighting mission success scenario. The outcomes of this research is revolutionary in terms of early identification of team success factors, providing support strategies for improvement of team performance and corresponding team situational awareness in tasks. We experimentally demonstrate that the introduction of Big Data analytics and sentiment analysis in human factor research can invariably boost the situational awareness and team communication dynamics nuances that might otherwise be overlooked. Thus, this research highlights a new avenue for leveraging the team communication dynamics for better situational awareness and mission success objectives.