Text Analytics and Sentiment Mining Using SAS®

The proliferation of textual data in business is overwhelming. Unstructured textual data are being constantly generated via call center logs, e-mails, documents on the Web, blogs, tweets, customer comments, customer reviews, and so on. While the amount of textual data are increasing rapidly, businesses' ability to summarize, understand, and make sense of such data for making better business decisions remain challenging. No marketing or customer intelligence program can be effective today without thoroughly understanding how to analyze textual data. Emphasizing practical skills as well as providing theoretical knowledge, this hands-on course takes a comprehensive look at how to organize, manage, and mine textual data for extracting insightful information from large collections of documents and using such information for improving business operations and performance.

Voraussetzungen
Some experience with SAS and SAS Enterprise Miner is useful, but it is not mandatory. No experience with text analysis is necessary.

Zielgruppe
Business analysts, web analysts, BI professionals, customer intelligence professionals, data analysts, market researchers, marketing analysts, social media analysts, text analysts, and data miners who want to learn how to effectively use text data to generate customer insights and to understand and predict customer sentiments

Module
SAS Enterprise Miner, SAS Text Analytics, SAS Text Miner, SAS Sentiment Analysis Studio

Kursinhalte

• Definition, History, and Architecture of Text Analytics
• Taxonomy, Basic Theory, and Pre-Processing of Text Data
• Classification and Entity Extraction from Text
• Feature Selection, Dimensionality Reduction, SVD in Text Analytics
• Clustering, Associative Networks, and Predictive Modeling in Text Analytics
• Definition and History of Sentiment Mining
• Data Mining, Natural Language Processing, and Hybrid Methods of Sentiment Analysis

Referent
This course will be held by Goutam Chakraborty, Ph.D., professor of marketing and founder of the SAS and Oklahoma State University Data Mining Certificate Program.

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