Text Analytics and Sentiment Mining Using SAS
Text Analytics and Sentiment Mining Using SAS Course Details:
Big data is unstructured, and a high volume of it is coming at you quickly. In fact, the majority of big data is unstructured and text oriented, thanks to the proliferation of online sources such as blogs, emails, and social media. While the amount of textual data are increasing rapidly, businesses' ability to summarize 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. In this course that emphasizes practical skills as well as providing theoretical knowledge, you will learn how to organize, manage, and mine textual data for extracting insightful information from large collections of documents and use such information for improving business operations and performance.
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1. Text Analytics and Sentiment Mining
- History and roots of text analytics
- SAS tools and components for text analytics
- Application areas of Text Analytics
2. Getting Textual Data into SAS Enterprise Miner and SAS Text Miner
- Introduction to SAS Enterprise Miner and SAS Text Miner
- Demonstration of the Text Import node for Web crawling
- Demonstration of the File Import node for reading Excel files
- Demonstration of XML mapper for reading XML files
- A brief introduction to SAS IR Studio (self study)
- Hands-on exercises to enhance learning all of the above concepts
3. Text Parsing and Using Term by Document Matrix
- Bag-of-words versus NLP approach in parsing
- Tokenization, lemmatization, POS Tags, phrase and entity recognition
- Weights and transformations used in handling term-by-document matrix
- Demonstration of text parsing, text filtering, text search, and concept links
- Hands-on exercises to enhance learning all of the above concepts
4. Clustering and Topic Extraction
- Different types of similarity metrics and methods used in clustering
- Different clustering algorithms available in SAS
- SVD and LSI for clustering textual data
- Differences between the the Text Cluster node and the Text Topic node
- Demonstration of Text Cluster and Text Topic extraction
- Hands-on exercises to enhance learning of all of the above concepts
5. Predictive Modeling and Text Rule Builder
- Predictive modeling essentials for text and numeric data
- Demonstration of Text Rule Builder node for model building using text data
- Demonstration of the Text Cluster node and the Text Topic node for model building using text data
- Demonstration of model building using both numeric and textual data
- Hands-on exercises to enhance learning all of the above concepts
6. Sentiment Analysis and Opinion Mining
- Basics of sentiment analysis
- Architecture and types of models in SAS Sentiment Analysis Studio
- Demonstration of statistical and rule-based model building in SAS Sentiment Analysis Studio
- Hands-on exercises to enhance learning all of the above concepts
7. Content Categorization, Concept Extraction, Wrap-Up, and Takeaways
- Basics of content categorization
- Architecture and types of rules supported in SAS Content Categorization Studio
- Demonstration of different models and rules in SAS Content Categorization Studio
- Hands-on exercises to enhance learning all of the above concepts
- LITI definitions (self study)
- Course wrap-up and takeaways
*Please Note: Course Outline is subject to change without notice. Exact course outline will be provided at time of registration.
Exercises or hands-on workshops are included with most SAS courses.
- Business analysts
- Web analysts
- BI professionals
- Customer intelligence professionals
- Data analysts
- Market researchers
- Marketing analysts
- Social media analysts
- Text analysts
- Data miners who want to learn how to effectively use text data to generate customer insights as well as understand and predict customer sentiments