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Using SAS™ Software to Enhance Pedagogy for Text Mining and Sentiment Analysis Using Social Media Twitter™) Data.

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Title: Using SAS™ Software to Enhance Pedagogy for Text Mining and Sentiment Analysis Using Social Media Twitter™) Data.
Authors: Subramanian, Ramesh1 ramesh.subramanian@quinnipiac.edu, Cote, Danielle1 danielle.cote@quinnipiac.edu
Source: Journal of International Technology & Information Management. 2018, Vol. 27 Issue 2, p73-98. 26p. 1 Color Photograph, 8 Diagrams, 10 Charts.
Subjects: SAS (Computer program language), Programming languages, Twitter (Web resource), Text mining, Sentiment analysis
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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of International Technology & Information Management is the property of International Information Management Association (IIMA), Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Using SAS™ Software to Enhance Pedagogy for Text Mining and Sentiment Analysis Using Social Media Twitter™) Data.
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  Data: <searchLink fieldCode="AR" term="%22Subramanian%2C+Ramesh%22">Subramanian, Ramesh</searchLink><relatesTo>1</relatesTo><i> ramesh.subramanian@quinnipiac.edu</i><br /><searchLink fieldCode="AR" term="%22Cote%2C+Danielle%22">Cote, Danielle</searchLink><relatesTo>1</relatesTo><i> danielle.cote@quinnipiac.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+International+Technology+%26+Information+Management%22">Journal of International Technology & Information Management</searchLink>. 2018, Vol. 27 Issue 2, p73-98. 26p. 1 Color Photograph, 8 Diagrams, 10 Charts.
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  Data: <searchLink fieldCode="DE" term="%22SAS+%28Computer+program+language%29%22">SAS (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Programming+languages%22">Programming languages</searchLink><br /><searchLink fieldCode="DE" term="%22Twitter+%28Web+resource%29%22">Twitter (Web resource)</searchLink><br /><searchLink fieldCode="DE" term="%22Text+mining%22">Text mining</searchLink><br /><searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink>
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  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of International Technology & Information Management is the property of International Information Management Association (IIMA), Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.58729/1941-6679.1380
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      – Code: eng
        Text: English
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        PageCount: 26
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      – SubjectFull: SAS (Computer program language)
        Type: general
      – SubjectFull: Programming languages
        Type: general
      – SubjectFull: Twitter (Web resource)
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      – SubjectFull: Text mining
        Type: general
      – SubjectFull: Sentiment analysis
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      – TitleFull: Using SAS™ Software to Enhance Pedagogy for Text Mining and Sentiment Analysis Using Social Media Twitter™) Data.
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              Text: 2018
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