Social Robot Modelling of Human Affective State
This addresses the need for social robots to understand human affective states for better interactions, but it appears incremental as it applies existing methods to new data.
The paper tackled the problem of inferring human affective states like mood, emotions, and personality from short text documents, which are surrogates for what social robots might hear, and reported surprisingly strong performance accuracy for personality dimensions.
Social robots need to understand the affective state of the humans with whom they interact. Successful interactions require understanding mood and emotion in the short term, and personality and attitudes over longer periods. Social robots should also be able to infer the desires, wishes, and preferences of humans without being explicitly told. We investigate how effectively affective state can be inferred from corpora in which documents are plausible surrogates for what a robot might hear. For mood, emotions, wishes, desires, and attitudes we show highly ranked documents; for personality dimensions, estimates of ground truth are available and we report performance accuracy. The results are surprisingly strong given the limited information in short documents.