Big data: it's unstructured, it's coming at you fast, and there's a lot of it. In fact, the majority of big data is unstructured and text oriented, thanks to the proliferation of online sources such as blogs, e-mails, and social media. 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.
- gain a deep understanding of tools and techniques of text analytics and sentiment mining from statistical and NLP (natural language processing) perspectives
- import text data into SAS Text Miner from different sources and different formats
- create clusters from text data to understand customer segments
- derive topics from text data to better understand customer conversation
- create rules from text data to make predictions
- combine text data with numeric data to build better models
- create statistical, rule-based, and hybrid models for understanding and predicting customer sentiments
- create statistical and rule-based models to categorize documents into a specified taxonomy
- create topics and word clouds, promote topics to categories, split or combine topics, and score new data using SAS Contextual Analysis.
Who Can Benefit
- 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
- Some experience with SAS and SAS Enterprise Miner is useful, but it is not mandatory. No experience with text analysis is necessary.