SPSS Modeler Text Analytics offers powerful text analytic capabilities, which use advanced linguistic technologies and Natural Language Processing (NLP) to rapidly process a large variety of unstructured text data and, from this text, extract and organize the key concepts. Furthermore, SPSS Modeler Text Analytics can group these concepts into categories.

Around 80% of data held within an organization is in the form of text documents—for example, reports, Web pages, e-mails, and call center notes. Text is a key factor in enabling an organization to gain a better understanding of their customers’ behavior. A system that incorporates NLP can intelligently extract concepts, including compound phrases. Moreover, knowledge of the underlying language allows classification of terms into related groups, such as products, organizations, or people, using meaning and context. As a result, you can quickly determine the relevance of the information to your needs. These extracted concepts and categories can be combined with existing structured data, such as demographics, and applied to modeling in IBM® SPSS® Modeler's full suite of data mining tools to yield better and more-focused decisions.

Linguistic systems are knowledge sensitive—the more information contained in their dictionaries, the higher the quality of the results. SPSS Modeler Text Analytics is delivered with a set of linguistic resources, such as dictionaries for terms and synonyms, libraries, and templates. This product further allows you to develop and refine these linguistic resources to your context. Fine-tuning of the linguistic resources is often an iterative process and is necessary for accurate concept retrieval and categorization. Custom templates, libraries, and dictionaries for specific domains, such as CRM and genomics, are also included.


In general, anyone who routinely needs to review large volumes of documents to identify key elements for further exploration can benefit from IBM SPSS Modeler Text Analytics.

Some specific applications include:


  • Scientific and medical research. Explore secondary research materials, such as patent reports, journal articles, and protocol publications. Identify associations that were previously unknown (such as a doctor associated with a particular product), presenting avenues for further exploration. Minimize the time spent in the drug discovery process. Use as an aid in genomics research.

  • Investment research. Review daily analyst reports, news articles, and company press releases to identify key strategy points or market shifts. Trend analysis of such information reveals emerging issues or opportunities for a firm or industry over a period of time.

  • Fraud detection. Use in banking and health-care fraud to detect anomalies and discover red flags in large amounts of text.

  • Market research. Use in market research endeavors to identify key topics in open-ended survey responses.

  • Blog and Web feed analysis. Explore and build models using the key ideas found in news feeds, blogs, etc.
  • CRM. Build models using data from all customer touch points, such as e-mail, transactions, and surveys.


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