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Accurate inferences of the emotional state of conversation participants can be critical in shaping analysis and interpretation of conversational exchanges. In qualitative analyses of discourse, most labelling of the perceived emotional state of conversation participants is performed by hand, and is limited to selected moments where an analyst may believe that emotional information is valuable for interpretation. This reliance on manual labelling processes can have implications for repeatability and objectivity, both in terms of accuracy, but also in terms of changes in emotional state that might go unnoticed. In this paper we introduce a qualitative discourse analytic support method intended to support the labelling of emotional state of conversational participants over time. We demonstrate the utility of the technique using a suite of well-studied broadcast interviews, taking a particular focus on identifying instances of inter-speaker conflict. Our findings indicate that this two-step machine learning approach can help decode how moments of conflict arise, sustain, and are resolved through the mapping of emotion over time. We show how such a method can provide useful evidence of the change in emotional state by interlocutors which could be useful to prompt and support further in-depth study. [ABSTRACT FROM AUTHOR]

Introduction: Pharmacist-patient communication during medication counselling has been successfully investigated using Communication Accommodation Theory (CAT). Communication researchers in other healthcare professions have utilised Discursis software as an adjunct to their manual qualitative analysis processes. Discursis provides a visual, chronological representation of communication exchanges and identifies patterns of interactant engagement. Aim: The aim of this study was to describe how Discursis software was used to enhance previously conducted qualitative analysis of pharmacist-patient interactions (by visualising pharmacist-patient speech patterns, episodes of engagement, and identifying CAT strategies employed by pharmacists within these episodes). Methods: Visual plots from 48 transcribed audio recordings of pharmacist-patient exchanges were generated by Discursis. Representative plots were selected to show moderate-high and low- level speaker engagement. Details of engagement were investigated for pharmacist application of CAT strategies (approximation, interpretability, discourse management, emotional expression, and interpersonal control). Results: Discursis plots allowed for identification of distinct patterns occurring within pharmacist-patient exchanges. Moderate-high pharmacist-patient engagement was characterised by multiple off-diagonal squares while alternating single coloured squares depicted low engagement. Engagement episodes were associated with multiple CAT strategies such as discourse management (open-ended questions). Patterns reflecting pharmacist or patient speaker dominance were dependant on clinical setting. Discussion and conclusions: Discursis analysis of pharmacist-patient interactions, a novel application of the technology in health communication, was found to be an effective visualisation tool to pin-point episodes for CAT analysis. Discursis has numerous practical and theoretical applications for future health communication research and training. Researchers can use the software to support qualitative analysis where large data sets can be quickly reviewed to identify key areas for concentrated analysis. Because Discursis plots are easily generated from audio recorded transcripts, they are conducive as teaching tools for both students and practitioners to assess and develop their communication skills. [ABSTRACT FROM AUTHOR]