Text Mining and the Text Analytics Sequence

Text analytics or text mining is the natural extension of predictive analytics. Text analytics is now ubiquitous and yields insight :

  • Marketing: Voice of the customer, social media analysis, churn analysis, market research, survey analysis
  • Business: Competitive intelligence, document categorization, human resources (voice of the employee), records retention, risk analysis, website faceted navigation
  • Industry specific: Fraud detection, e-discovery, warranty analysis, medical analytics research


1: Text Mining
Are you prepared?
You may already have the machine learning and python skills needed for text analytics; if not you can learn them in:
Learn to pilot, implement or analyze data mining methods aimed at data containing unstructured text (forms, surveys, etc.).

Introduction to the algorithms, and software used natural language processing (NLP)

Introduce natural language processing (NLP) processes into your projects and software applications. NLTK provides cutting edge linguistic and machine learning tools that are on par with traditional NLP frameworks and allows you to quickly and easily analyze text data larger applications.

Introduction to the algorithms, and software used sentiment analysis, illustrated by reference to existing applications, particularly product reviews and opinion mining.

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