Knowledge encapsulation framework for technosocial predictive modeling

Abstract

Analysts who use predictive analytics methods need actionable evidence to support their models and simulations. Commonly, this evidence is distilled from large data sets with significant amount of culling and searching through a variety of sources including traditional and social media. The time/cost effectiveness and quality of the evidence marshaling process can be greatly enhanced by combining component technologies that support directed content harvesting, automated semantic annotation, and content analysis within a collaborative environment, with a functional interface to models and simulations. Existing evidence extraction tools provide some, but not all, the critical components that would empower such an integrated knowledge management environment. This paper describes a novel evidence marshaling solution that significantly advances the state of the art. Its embodiment, the Knowledge Encapsulation Framework (KEF), offers a suite of semi-automated and configurable content harvesting, vetting, annotation and analysis capabilities within a wiki-enabled and user-friendly visual interface that supports collaborative work across distributed teams of analysts. After a summarization of related work, our motivation, and the technical implementation of KEF, we will explore the model for using KEF and results of our research.

Sentiment Analysis in Supply Chain Management

“…Thus, sentiment analysis allows encapsulation of positive or negative responses and changes (Madison et al, 2012) and understanding the strength of expression, following a supervised classification task (Taboada et al, 2011). Sentiment analysis has three constituent tasks: (1) subjectivity classification, (2) the sentiment classification, (3) and either opinion holder extraction or object/feature extraction, depending on the Analyst's requirements.…”