Sentiment and behaviour annotation in a corpus of dialogue summaries

This paper proposes a scheme for sentiment annotation. We show how the task can be made tractable by focusing on one of the many aspects of sentiment: sentiment as it is recorded in behaviour reports of people and their interactions. Together with a number of measures for supporting the reliable application of the scheme, this allows us to obtain sufficient to good agreement scores (in terms of Krippendorf's alpha) on three key dimensions: polarity, evaluated party and type of clause. Evaluation of the scheme is carried out through the annotation of an existing corpus of dialogue summaries (in English and Portuguese) by nine annotators. Our contribution to the field is twofold: (i) a reliable multi-dimensional annotation scheme for sentiment in behaviour reports; and (ii) an annotated corpus that was used for testing the reliability of the scheme and which is made available to the research community

Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie