Enhancing Sentiment Analysis and Error Prediction in Lawyer Questioning Through Legal NLP Models: A Mixed-Methods Study in Australian Courtrooms

This mixed methods study investigates the use of natural language processing (NLP) to analyze sentiment and predict interpretation errors in bilingual court interpreting, focusing on Mandarin-English remote hearings. We assembled a dataset of 3,250 minutes of courtroom recordings and 192,465 utterances, annotated for sentiment and error types. Our methodology combines transformer-based sentiment analysis with Conditional Random Fields and Support Vector Machines for error prediction, leveraging linguistic features such as discourse markers, hedging devices, and textual tonal cues. Sentiment analysis reveals that domain-specific fine-tuning of transformer models captures subtle emotional shifts in lawyer questioning with high accuracy, yet interpreters frequently omit critical markers and tonal signals, diluting intended rhetorical force, particularly during cross-examination. Error prediction shows that simultaneous interpretation incurs significantly higher omission and distortion rates than consecutive interpreting, and that aggressive questioning markedly increases error likelihood. This research integrates quantitative NLP modeling with qualitative discourse analysis, offering insights into cognitive and pragmatic factors that affect interpretation fidelity. By highlighting patterns of sentiment shift and interpreter error, our findings inform targeted training and technological interventions aimed at promoting linguistic equity and procedural fairness in multilingual judicial contexts. This article contributes to mixed methods methodology by (i) specifying an explanatory sequential design in which quantitative error metrics and sentiment modeling purposively inform reflexive thematic analysis of interpreter accounts, and (ii) offering a joint display that integrates Quantitative patterns (e.g., omission rates by mode × tone) with Qualitative themes (e.g., cognitive load management and discourse-marker decisions), thereby illustrating how integration at interpretation yields inferences unattainable by monomethod designs.