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Investigating the Relationship Between Emotion Recognition Software and Usability Metrics

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De Gruyter


Due to progress in affective computing, various forms of general purpose sentiment/emotion recognition software have become available. However, the application of such tools in usability engineering (UE) for measuring the emotional state of participants is rarely employed. We investigate if the application of sentiment/emotion recognition software is beneficial for gathering objective and intuitive data that can predict usability similar to traditional usability metrics. We present the results of a UE project examining this question for the three modalities text, speech and face. We perform a large scale usability test (N = 125) with a counterbalanced within-subject design with two websites of varying usability. We have identified a weak but significant correlation between text-based sentiment analysis on the text acquired via thinking aloud and SUS scores as well as a weak positive correlation between the proportion of neutrality in users’ voice and SUS scores. However, for the majority of the output of emotion recognition software, we could not find any significant results. Emotion metrics could not be used to successfully differentiate between two websites of varying usability. Regression models, either unimodal or multimodal could not predict usability metrics. We discuss reasons for these results and how to continue research with more sophisticated methods.


Schmidt, Thomas; Schlindwein, Miriam; Lichtner, Katharina; Wolff, Christian (2020): Investigating the Relationship Between Emotion Recognition Software and Usability Metrics. i-com: Vol. 19, No. 2. DOI: 10.1515/icom-2020-0009. Berlin: De Gruyter. PISSN: 2196-6826. pp. 139-151