Emerging Named Entity Recognition on Retrieval Features in an Affective Computing Corpus Affective Computing (AC) is a relatively new, dynamic and interdisciplinary research field. Numerous contributions from fields like computer science, psychology, cognitive science, sociology, physiology and medical science have been made. Consequently, it is difficult to track all recently published trends for early insight utilisation in practise or as basis for innovative research. Even if this fact holds true for many other research fields, AC in this respect is stimulating, due to its dynamic and interdisciplinary characteristics. However, Emergent Entities Recognition is a new concept introduced for early detection and prediction of developing professional terminology. Initial software developments have been completed and briefly analysed in general databases (e.g. MEDLINE). Here, we are interested in its evaluation for AC. In this respect, we have created and used a new Benchmark for Emergent Entities recognition specifially for the field of AC and show evaluation results in comparison to state of the art trained named entity recognition models and to a generic corpus (MEDLINE).

Using an Affective Computing Taxonomy Management System to Support Data Management in Personality Traits Affective Computing is a rather new and multidisciplinary research field that seeks sophisticated automation in emotion detection for later analysis. However, the automated emotion detection and analysis require as well comprehensive data management support, e.g. to keep control of data produced, and to enable its efficient reuse through classification with established terminology. This paper contributes to data management aspects in Affective Computing and to automation support in emotion classification on the basis of a personal traits analysis. Hence, we describe the implementation of a taxonomy management system, derived from requirements of a case study that investigates the relationship between personality and emotions in Affective Computing. The study makes use of machine learning software developed by SenseCare, an EUfunded Ru0026D project that applies Affective Computing to enhance and advance future healthcare processes and systems.