Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applications.

Sentic computing relies on well-defined affective models of different complexity-polarity to distinguish positive and negative sentiment, for example, or more nuanced models to capture expressions of human emotions. When used to measure communication success, even the most granular affective model combined with sophisticated machine learning approaches may not fully capture an organisation's strategic positioning goals. Such goals often deviate from the assumptions of standardised affective models. While certain emotions such as Joy and Trust typically represent desirable brand associations, specific communication goals formulated by marketing professionals often go beyond such standard dimensions. For instance, the brand manager of a television show may consider fear or sadness to be desired emotions for its audience. This article introduces expansion techniques for affective models, combining common and commonsense knowledge available in knowledge graphs with language models and affective reasoning, improving coverage and consistency as well as supporting domain-specific interpretations of emotions. An extensive evaluation compares the performance of different expansion techniques: (i) a quantitative evaluation based on the revisited Hourglass of Emotions model to assess performance on complex models that cover multiple affective categories, using manually compiled gold standard data, and (ii) a qualitative evaluation of a domain-specific affective model for television programme brands. The results of these evaluations demonstrate that the introduced techniques support a variety of embeddings and pre-trained models. The paper concludes with a discussion on applying this approach to other scenarios where affective model resources are scarce.

Enabling the European Patient Summary through triplespaces.

One of the main items on the eHealth agenda of the European Community is the design and promotion of electronic patient summaries as an instrument to guarantee the continuity of healthcare across national boundaries and to facilitate the pervasive delivery of healthcare, thus ensuring the right to patient mobility and increasing the productivity and quality of health service delivery. From a technical point of view, this objective requires middleware technology which is able to cope with the stringent interoperability, multi-lingualism, distribution, and privacy requirements arising in eHealth settings. This paper presents triplespace computing, a coordination middleware for the Semantic Web,and demonstrates its relevance to the realization of the European Patient Summary, an infrastructure for sharing and coordinating access to patient summaries at the European level.