Visualizing Term Eigenvector Prominence in a Corporate Social Responsibility Context

  • Carlos M Parra Florida International University, United States
  • Monica Tremblay Florida International University, United States
  • Arturo Castellanos City University of New York, United States

Abstract

In this study we develop a simplified technique for helping researchers and analysts visualize the alternative prominence of term eigenvectors obtained after exploring term associations (Term Clusters) while conducting Text Data Mining on a collection to Corporate Social Responsibility (CSR) reports. The collection analyzed is comprised of CSR reports produced by 7 US firms (Citi, Coca-Cola, Exxon-Mobil, General Motors, Intel, McDonald’s and Microsoft) in 2004, 2008 and 2012. The analysis is performed by year in order to discern how the prominence of term eigenvectors has evolved for each firm and for different CSR topics. Results indicate that term eigenvectors maintain their prominence when CSR topics are related to the core business of the firm in question.

Author Biographies

Carlos M Parra, Florida International University, United States

Clinical Professor

Department of Information Systems and Business Analytics

College of Business

Monica Tremblay, Florida International University, United States

Department Chair

Department of Information Systems and Business Analytics

College of Business

 

Arturo Castellanos, City University of New York, United States

Assistant Professor

Department of Information Systems and Statistics

Zicklin School of Business,

 

Published
2016-12-21
How to Cite
PARRA, Carlos M; TREMBLAY, Monica; CASTELLANOS, Arturo. Visualizing Term Eigenvector Prominence in a Corporate Social Responsibility Context. Journal on Advances in Theoretical and Applied Informatics, [S.l.], v. 2, n. 2, p. 31-37, dec. 2016. ISSN 2447-5033. Available at: <http://revista.univem.edu.br/jadi/article/view/2092>. Date accessed: 16 dec. 2017.