Text Mining in Big Data Analytics Minitrack

Global collaborations, social media, and information systems of all types, generate enormous amounts of textual data (e.g. email archives, websites, blog posts, meeting transcripts, speeches, annual reports, published material, and social media posts). While this unstructured textual data is readily available, it presents a tremendous challenge to researchers trying to analyze these large bodies of text with traditional social science methods. Text mining in big data analytics is becoming increasingly important to an interdisciplinary group of scholars, practitioners, government officials, and international organizations. For example, the American Association for the Advancement of Science (AAAS) launched a new competition in 2014 on Big Data and Analytics within its highly competitive senior executive branch fellowship program. Other corporate initiatives like the Big Boulder Initiative was formed in 2014 as a trade association wholly dedicated to "the advancement of social data in businesses and organizations of all kinds" (http://www.bbi.org/).

However, within this growing focus on Big Data, there is a dilemma. While as much as 75-80% of available data is unstructured text, many people are not trained in the techniques for analyzing large bodies of text. This minitrack is designed to contribute to the growing big data focus at HICSS, and invites papers that apply text-mining approaches to a wide variety of substantive domains, including, but not limited to theoretical and applied approaches to analyzing:

  • Blog posts
  • Twitter and social media analysis
  • Email archives
  • Published articles
  • Websites and blogs
  • Meeting transcripts
  • Speeches

And addressing methodological challenges, such as:

  • Automated acquisition and cleaning data
  • Working on distributed, high-performance computers
  • Overcoming API limitations
  • Using LDA, LSA, and other techniques

Minitrack Co-Chairs:

Derrick L. Cogburn (Primary Contact)
American University
Email: dcogburn@american.edu

Michael J. Hine
Carleton University
Email: mike.hine@carleton.ca

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