This paper explores how computational analysis of paralinguistic cues, particularly breathing, can enrich the study of emotionality in Oral History Archives (OHA). While oral historians recognise the loss of non-verbal information in transcript-based research, large-scale audio analysis remains underdeveloped. Using the Voices of ACT UP (VO-ACTUP) dataset, which contains interviews with ACT UP activists about their experiences during the AIDS epidemic, we investigate how non-verbal audio features capture emotional nuances that transcripts cannot convey. To support this analysis, we implement and extend state-of-the-art speech-based breathing prediction models using the UCL-SBM dataset and propose two new WavLM-based architectures that improve current performance. We then assess robustness by aligning predicted breathing signals with annotated breathing events in VO-ACTUP. Finally, we examine how breathing-derived features, such as breathing rate, correlate with emotional expression across the corpus. Our work demonstrates the potential of breathing analysis to reveal affective dimensions of oral history at scale.