Journal of International Technology and Information Management
Document Type
Article
Abstract
This pedagogical paper describes how a graduate course in Text Mining was developed and taught in a fully online format at Quinnipiac University. The software used was SAS™ Enterprise Miner. This paper discusses the design, software used and the methodology followed in the course. A critical component of the course required the students to delve deep into social media data by completing a detailed project on analyzing sentiment analysis using large files of social media data. A sample report of this project, which was a key deliverable for the course, is described at length in this paper.
Recommended Citation
Subramanian, Ramesh and Cote, Danielle
(2018)
"Using SAS™ software to enhance pedagogy for Text Mining and Sentiment Analysis using social media (Twitter™) data,"
Journal of International Technology and Information Management: Vol. 27:
Iss.
2, Article 4.
DOI: https://doi.org/10.58729/1941-6679.1380
Available at:
https://scholarworks.lib.csusb.edu/jitim/vol27/iss2/4
Included in
Business Intelligence Commons, Curriculum and Instruction Commons, Management Information Systems Commons, Online and Distance Education Commons, Social Media Commons, Technology and Innovation Commons
