Abstract:
Opinion mining which is a sub discipline within data mining and computational linguistics refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online news sources, social media comments, and other user-generated content is discussed. Frameworks and methods for integrating sentiments and opinions expressed with other computational representations such as interesting topics or product features extracted from user-generated text, participant reply networks, spikes and outbreaks of ideas or events are also critically needed. Disagreement and subjectivity also held significant relationships with volatility, where less disagreement and high levels of subjectivity predicted periods of high stock volatility. Positive sentiment reduces trading volume, perhaps because satisfied shareholders hold their stock, while negative sentiment induces trading activity as shareholders defect.
Abstract:
To strengthen homeland security, there is a critical need for new tools that can facilitate real time collaboration among various law enforcement agencies. Through a field study, we find that law enforcement work is knowledge intensive and involves complex collaborative processes interrelating a large number of disparate units in a loosely defined virtual organization. To support knowledge intensive collaboration, we propose a new workflow centric framework to seamlessly integrate previously separate techniques from the fields of information retrieval and workflow management. Specifically, we develop a collaborative workflow management framework for interagency crime analysis. The key contribution of our research is that by integrating various state-of-the-art techniques innovatively, the proposed system can support real time collaboration processes in a virtual organization that evolves dynamically. © Springer-Verlag Berlin Heidelberg 2003.
Abstract:
In summary, the special issue papers address very interesting and relevant issues related to Cyberinfrastructure for homeland security. It has been a privilege to guest edit this issue and be involved in the intellectual endeavors of researchers at the fore front of these efforts. We especially thank Professor Andrew Whinston, Editor-in-chief of Decision Support Systems, for giving us this opportunity and thank all the reviewers for their diligent effort in ensuring the quality of the papers. We thank all the authors for contributing their work to the special issue and bearing with us on some delays in the review process. We hope the readers share our enthusiasm for the papers published in this issue and for their relevance in advancing novel innovations in information systems specifically targeted to counterterrorism efforts. © 2006 Elsevier B.V. All rights reserved.
Abstract:
Analyzing market performance via social media has attracted a great deal of attention in the finance and machine- learning disciplines. However, the vast majority of research does not consider the enormous influence a crisis has on social media that further affects the relationship between social media and the stock market. This article aims to address these challenges by proposing a multistage dynamic analysis framework. In this framework, we use an authorship analysis technique and topic model method to identify stakeholder groups and topics related to a special firm. We analyze the activities of stakeholder groups and topics in different periods of a crisis to evaluate the crisis's influence on various social media parameters. Then, we construct a stock regression model in each stage of crisis to analyze the relationships of changes among stakeholder groups/topics and stock behavior during a crisis. Finally, we discuss some interesting and significant results, which show that a crisis affects social media discussion topics and that different stakeholder groups/topics have distinct effects on stock market predictions during each stage of a crisis. © 2013 Science China Press and Springer-Verlag Berlin Heidelberg.
