Identifying Emotions in Social Media: Comparison of Word-emotion lexica

In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion lexicons (NRC, DepecheMood, EmoSenticNet) over a set of Facebook and Twitter messages. To this end, we designed and implemented an algorithm that applies natural language processing (NLP) techniques along with a number of heuristics that reflect the way humans naturally assess emotions in written texts. In order to evaluate the appropriateness of the obtained emotion scores, we conducted a questionnaire-based survey with human raters. Our results show that there are noticeable differences between the performance of the lexicons as well as with respect to emotion scores the human raters provided in our surve

Defining and Analysing Resource Assignments in Business Processes with RAL

Business process (BP) modelling notations tend to stray their attention from (human) resource management, unlike other aspects such as control flow or even data flow. They not only offer little intuitive languages to assign resources to BP activities, but neither link BPs with the structure of the organization where they are used, so BP models can easily contain errors such as the assignment of resources that do not belong to the organizational model. In this paper we address this problem and define RAL (Resource Assignment Language), a domainspecific language explicitly developed to assign resources to the activities of a BP model. RAL makes BPs aware of organizational structures. Besides, RAL semantics is based on an OWL-DL ontology, which enables the automatic analysis of resource assignment expressions, thus allowing the extraction of information from the resource assignments, and the detection of inconsistencies and assignment conflicts

Priority-Based Human Resource Allocation in Business Processes

In Business Process Management Systems, human resource management typically covers two steps: resource assignment at design time and resource allocation at run time. Although concepts like rolebased assignment often yield several potential performers for an activity, there is a lack of mechanisms for prioritizing them, e.g., according to their skills or current workload. in this paper, we address this research gap. More specifically, we introduce an approach to define resource preferences grounded on a validated, generic user preference model initially developed for semantic web services. Furthermore, we show an implementation of the approach demonstrating its feasibility. Keywords: preference modeling, preference resolution, priority-based allocation, priority ranking, RAL, resource allocation, SOUP

RALph: A Graphical Notation for Resource Assignments in Business Processes

The business process (BP) resource perspective deals with the management of human as well as non-human resources throughout the process lifecycle. Although it has received increasing attention recently, there exists no graphical notation for it up until now that is both expressive enough to cover well-known resource selection conditions and independent of the BP modelling language. In this paper, we introduce RALph, a graphical notation for the assignment of human resources to BP activities. We define its semantics by mapping this notation to a language that has been formally defined in description logics, which enables its automated analysis. Although we show how RALph can be seamlessly integrated with BPMN, it is noteworthy that the notation is independent of the BP modelling language. Altogether, RALph will foster the visual modelling of the resource perspective in BP