What question did this study set out to answer?

To evaluate the effectiveness of AI-driven software in analyzing verbal and non-verbal expressions of emotive modality in German.

May 3, 2026Open Access

The role of AI in multimodal research on the verbal and non-verbal means of expressing emotive modality in German: an investigation into selecting and analysing data

Key Points

To evaluate the effectiveness of AI-driven software in analyzing verbal and non-verbal expressions of emotive modality in German.
Investigated audio and visual data types using two AI software programs for linguistic analysis.
Analyzed how lexeme-specific sentential scopes affect data applicability in emotive expression research.
Conducted empirical analysis to determine the detection of emotions across both data types.
AI software detected a higher number of emotions in both audio and visual data compared to traditional methods.
The study confirmed the necessity of using entire sentences for accurate emotive modality analysis.
Significant correlations were observed between verbiage and non-verbal cues like intonation and gesture.

Abstract

Abstract This paper seeks to substantiate the utility of AI-driven software in multimodal research. It endeavours to elucidate the pre-eminence of two specific pieces of software over alternatives used in linguistic analyses of this kind. The present study thoroughly scrutinises the eligibility of two sorts of audio and two types of visual data for automatically investigating the correlation of verbal and non-verbal means of expressing emotive modality in the German language. In doing so, it examines how the sentential scope typical of certain lexemes influences the applicability of the data types under investigation. The empirical analysis corroborated the necessity to conduct research of this kind employing entire sentences featuring a lexeme characterised by sentential scope. Intonation patterns and nonmanual features recognised and investigated by the software can occur throughout uttering a sentence which features a lexeme under study. This conclusion is drawn on the basis of the greatness of the quantity of emotions detected by the software vis-à-vis both the audio and the visual data employed in the analysis.

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