SAS® Text Miner Papers and Presentations
2016 Papers
- Extending the Armed Conflict Location and Event Data Project with SAS Contextual Analysis
Sabo, Tom; SAS Institute, Inc. 2016This paper showcases a repeatable combination of exploratory and classification-based text analytics provided by SAS Contextual Analysis, applied to the publicly available ACLED for African states.
- Getting More from the Singular Value Decomposition (SVD): Enhance Your Models with Document, Sentence,
and Term Representations
Albright, Russell; Cox, James; Ning, Jin; SAS Institute, Inc. 2016In this presentation, using SAS code and SAS Text Miner, we compare supervised and unsupervised models with those that are based on SVD representations of subcomponents of documents.
2015 Papers
- Using Boolean Rule Extraction for Taxonomic Text Categorization for Big Data
Zhao, Zheng; Albright, Russ; Cox, James; Jin, Ning; SAS Institute, Inc. 2015This paper demonstrates how to use SAS Text Miner macros and procedures to obtain effective predictive models at all hierarchy levels in a taxonomy.
2014 Papers
- Analysis of Unstructured Data: Applications of Text Analytics and Sentiment Mining
Chakraborty, Goutam; Oklahoma State University; Pagolu, Murali Krishna; SAS Institute, Inc. 2014This paper takes a quick look at how to organize and analyze textual data for extracting insightful customer intelligence from a large collection of documents and for using such information to improve business operations and performance.
- Automatic Detection of Section Membership for SAS Conference Paper Abstract Submissions: A Case Study
Chakraborty, Goutam; Oklahoma State University; Pagolu, Murali Krishna; SAS Institute, Inc. 2014SAS Text Miner 12.1 and SAS Content Categorization Studio 12.1 is used to develop a rule-based categorization model. This model is then used to automatically score a paper abstract to identify the most relevant and appropriate conference sections to submit to for a better chance of acceptance.
- How to Interpret SVD Units in Predictive Models?
Pagolu, Murali Krishna; SAS Institute Inc.; Chakraborty, Goutam Dr.; Oklahoma State University, 2014This paper demonstrates a new and powerful feature in SAS Text Miner 12.1 which helps in explaining the SVDs or the text cluster components. Discussed also are two important methods useful to interpret them.
- Processing and Storing Sparse Data in SAS Using SAS Text Miner Procedures
Zhao, Zheng; Albright, Russell; Cox, James; SAS Institute Inc., 2014This paper demonstrates how to use SAS Text Miner procedures to process sparse data sets and generate output data sets that are easy to store and can be readily processed by traditional SAS modeling procedures.
2013 Papers
- Big Data Meets Text Mining
Zhao, Zheng; Albright, Russell; Cox, James; Bieringer, Alicia; SAS Institute, Inc. 2013This paper introduces high-performance text mining technology for SAS High- Performance Analytics.
- Enabling SAS High-Performance Text Mining Procedures – Description and Tips
SAS Institute, Inc. 2013The ever increasing need to process greater amounts of unstructured text data in less time to improve predictive modeling are addressed with high performance enablement of text mining methods. This paper describes the new high-performance procedures that can be used to decrease the time needed for your text mining tasks.
- Relate, Retain, and Remodel: Creating and Using Context-Sensitive Linguistic Features in Text Mining Models
Albright, Russell; Punuru, Janardhana; Surratt, Lane; SAS Institute, Inc. 2013This paper first teaches you how to write pattern-matching rules in SAS Enterprise Content Categorization and then shows you how to apply these patterns as a parsing step in SAS Text Miner.
- A Tale of Two SAS Technologies—Generating Maps of Topical Coverage and Linkages in SAS User Conference Papers
Bedford, Denise, Kent State University; de Ville, Barry, SAS Institute, Inc.; la Valley, Rich, Strategic Technology Solutions 2013This paper discusses how two SAS technologies – Text Analytics and Content Categorization Suite -- were used to generate comprehensive and dynamic categories and clusters of the entire corpus of SAS user presentations from inception to the present.
- Unleashing the Power of Unified Text Analytics to Categorize Call Center Data
Barnes, Arila; Peterson, Jared; Sethi, Saratendu; SAS Institute, Inc. 2013This paper demonstrates how a business analyst in a call center environment can identify emerging topics, generate automatic rules for those topics, edit and refine those rules to improve results, derive insights through visualization, and deploy the resulting model to score new data.
2012 Papers
- Correlating the Analysis of Opinionated Text Using SAS Text Analytics with Application of Sabermetrics to Cricket Statistics
Lakkaraju, Praveen; Sethi, Saratendu; SAS Institute, Inc. 2012This paper presents the results of experiments with applying Sabermetrics-style principles to the game of cricket and correlating the findings with analysis of opinionated text.
2011 Papers
- What Are People Saying About Your Company, Your Products, or Your Brand?
Lange, Kathy; Sethi, Saratendu; SAS Institute, Inc. 2011This paper discusses the use of SAS Sentiment Analysis and SAS Text Miner to uncover good and bad feedback. It discusses lessons learned from real projects.
2010 Papers
- Listening to the Twitter Conversation
Albright, Russell; Foley, Richard; Devarajan, Ravi ; SAS Institute, Inc. 2010This paper discusses the use of text mining, visualization, and the HTTP Procedure to provide a complete understanding of the Twitter conversation.
2009 Papers
- Discover and Drive Brand Activity in Social Networks
de Ville, Barry; SAS Institute, Inc. 2009This paper demonstrates how Web-based textual media can be mined and visualized to gain brand knowledge and customer leverage. Techniques—based on the interplay of SAS Text Miner and SAS Content Categorization powered by Teragram— are demonstrated as are social networks metrics calculations. Reports and exploratory social network visualizations through SAS/GRAPH® Network Visualization Workshop are presented.
2008 Papers
- Coming to a Theater Near You! Sentiment Classification Techniques Using SAS Text Miner
Bartlett, Jake; Albright, Russ; SAS Institute, Inc. 2008This paper demonstrates how different techniques for classifying movie reviews can be implemented in SAS Enterprise Miner and SAS Text Miner.
2007 Papers
- Making Web Results Relevant with SAS
Albright, Russell; Bartlett, Jake; Bultman, David; SAS Institute, Inc. 2007This paper presents a SAS-based solution to accessing and clustering Yahoo! search engine results by using SAS Text Miner.
Papers are in Portable Document Format (PDF) and can be viewed with the free Adobe Acrobat Reader.
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Powerpoint presentations and SAS programs can be downloaded as zip files.