Improve Insights with Text Mining
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Improve Insights with Text Mining

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Improve Insights with Text Mining Improve Insights with Text Mining Document Transcript

  • Text Mining for Clementine® 5.0 – Specifications Improve Insights with Text Mining SPSS Inc.’s Clementine data mining workbench enables Using a proven natural language processing (NLP) linguistic organizations to achieve measurable results by basing extraction process powered by SPSS Inc.’s LexiQuest™ decisions on patterns and associations found in their data. technology, Text Mining for Clementine pulls key concepts But did you know that up to 80 percent of your organization’s from many types of unstructured data and groups them data is contained in textual form? into categories. Extracted concepts and categories are then combined with structured data and applied to predictive The customer e-mails, call center notes, open-ended survey models to provide valuable insights into actions, behaviors, responses, Web forms, and other text sources that your patterns, and associations. organization captures—including content from RSS feeds, such as blogs and news feeds—contain up to four times as In addition, Text Mining for Clementine identifies and much valuable data as your organization’s structured data extracts sentiments, such as preferences and opinions, stores. This means that data mining projects focusing only which help to create more in-depth predictive models on structured data may use less than 20 percent of the and more accurate results. information available. Text Mining for Clementine enables you to combine this valuable unstructured data with traditional structured data to significantly increase your understanding of customers, the public, and other groups. This product transforms Clementine into a fully integrated data and text mining workbench. You can perform both text mining and data mining within the interactive, visualization-based Clementine environment.
  • Add value throughout your organization Improve predictive quality and accuracy Text mining can be used in nearly any business or research The value of analyzing a combination of structured and situation that involves unstructured data. Here are just unstructured data is both measurable and significant. some examples of Text Mining for Clementine applications: A predictive model that is based on 100 percent of the n Product development and refinement. Identify trends available information is much more likely to provide in complaints or requests by analyzing call center logs, accurate results than one based on only 20 percent or customer e-mails, open-ended survey responses, and less of the data. RSS feeds, including blogs. Use this information to improve existing products and services and develop A mobile telecommunications provider, for example, used successful new offerings. concepts extracted from its call center notes to improve the n Marketing campaigns. Improve campaign effectiveness performance of existing churn models by 10 to 50 percent. and revenue. For example, analyze inbound customer A financial services organization—concerned about calls in real time to provide better product and service potentially non-compliant stock trades—created a model recommendations. that tied information in internal e-mails to transactional n Churn prevention. Discover why some customers leave— data to predict which traders were most likely to break and take steps to prevent defection—by analyzing regulatory rules. By using Clementine and Text Mining for customer communication records for recurring problems Clementine together, you integrate text mining directly or complaints that precede churn. into the analytical process, and ensure a measurable n Cross-selling. Improve sales by using information about improvement in performance and results. customer preferences to better target products to existing customers. Unlock the power of text—no linguists required n Drug discovery. Find relationships in chemical and Unlike other text mining products, you do not need a biomedical databases. linguistic background or special training to use Text Mining n Competitive intelligence. Survey competitor and industry for Clementine. And because Text Mining for Clementine Web sites, RSS feeds, including blogs and news feeds, uses an interactive interface within Clementine, text mining and other publicly available textual information to is straightforward and efficient. For example, interactive maintain a current view of your competitors. graphs enable you to explore and display text data and n Security. Discover potentially suspect behavior by patterns for instant analysis. analyzing Web site content, chat rooms, e-mails, blogs, and other sources of information, and identify patterns You can easily customize concept dictionaries for a and associations in the data. particular domain area by using the Resource Editor, an integrated resource for managing the text extraction process. This enables you to find relevant concepts and associations faster.
  • For example, a company that wants to analyze call center You can also use insights derived from text data to improve notes can use the Resource Editor to adjust Text Mining real-time and batch scoring in SPSS Inc.’s PredictiveMarketing™ for Clementine’s dictionaries to reflect acronyms, marketing campaign optimization application and to provide abbreviations, and slang typically found in call transcripts. targeted real-time recommendations to inbound callers Or a pharmaceutical company can use the Resource Editor through PredictiveCallCenter™. Insurance companies can to set Text Mining for Clementine’s included genomics improve claim processing with PredictiveClaims™. dictionary as its default. If your organization collects customer insight through an Deploy into operational systems enterprise feedback management (EFM) solution, Text In order to make the best use of your textual data, you Mining for Clementine can help you understand the need to be able to use it throughout your organization. opinions, attitudes, and preferences of your customers, Text insights deployed through Clementine predictive employees, citizens, business partners, and others. models to operational databases provide value to areas throughout your organization. And you can deploy any To help manage your analytical assets and analytical part of the text or data mining process by using Clementine processes, use Text Mining for Clementine with SPSS Solution Publisher Runtime . ™ Predictive Enterprise Services™. Text Mining for Clementine’s deployment capabilities make textual insights available to business users in critical areas, so your entire organization benefits from a comprehensive, 360-degree view of customers or, for government agencies, the citizens they serve.
  • Text Mining for Clementine features* Web feed node Resource Editor Methodology n Easily retrieve and analyze text from RSS n Create and edit custom libraries directly Clementine and Text Mining for Clementine feeds, such as blogs and news feeds, in the Text Mining for Clementine interface support the CRoss-Industry Standard Process and HTML pages n Define and edit: for Data Mining (CRISP-DM), which enables – Domain-specific terms analysts to focus on solving business problems, Text mining modeling node – Non-linguistic entities rather than on programming. Text Mining for n Create clusters based on term co-occurrence – Synonyms Clementine enables you to merge unstructured using concept clustering algorithms, which – Concept libraries data with structured data during the CRISP-DM provide an at-a-glance view of main topics n Edit the CRM, opinion, competitive process. In addition, Text Mining for Clementine and the way in which they are related intelligence, security intelligence, uses a proven combination of natural language n Intelligently group text documents and and genomics dictionaries processing (NLP) techniques and predictive records based on content, using text analytics to efficiently extract meaningful classification algorithms Deployment information from unstructured data. – Aggregates concepts from a wide variety n Deploy the entire data mining process of unstructured text data and groups automatically with Clementine Solution Linguistic extraction them into a small number of categories Publisher Runtime n Extract text data from files or a database – Reuses categories, enabling the scoring n Send text mining extractions directly n Work with multilingual text. Select from of any new text documents and records to Clementine for export into seven native language extractor options: based on the text they contain PredictiveMarketing, PredictiveCallCenter, – Dutch – Accelerates and improves data or PredictiveClaims for scoring – English management n To help manage your analytical assets and – French – Includes term inclusion and derivation analytical processes, use Text Mining for – German lexical algorithms Clementine with SPSS Predictive Enterprise – Italian n Enable advanced concept selection Services – Portuguese and deselection for use in subsequent – Spanish Clementine predictive modeling Open integration n Translate up to 14 languages using applications n Easily integrates external linguistic resources Language Weaver add-on options n List extracted concepts by type, frequency, n Accesses any text file or relational database n Manage errors in punctuation and spelling document coverage, and other user-defined via Clementine’s high-performance ODBC n Extract domain-specific concepts such as classifications drivers uniterms, expressions, abbreviations, n Highlight synonyms used for each selected n Supports the industry-standard Predictive acronyms, and more concept Modeling Markup Language (PMML) n Calculate synonyms using sophisticated n Convert selected concepts to structured n Supports standard text document formats, linguistic algorithms and embedded or form for use in Clementine predictive including plain text, PDF, HTML, Microsoft® user-specified linguistic resources modeling algorithms Office, and XML n Name concepts by person, organization, n Works with multilingual text natively term, product, location, and other user- Text link analysis (Dutch, English, French, German, Italian, defined types n Identify and extract sentiments (for Portuguese, and Spanish) and uses n Extract non-linguistic entities such as example, likes and dislikes) in text translations via Language Weaver options address, currency, time, phone number, n Identify links and associations between, and social security number (SSN) for example, people and events, or – Templates for non-linguistic entities diseases and genes are available for all seven languages n Include opinions, semantic relationships, n Included opinion, competitive intelligence, and linked events in deployable predictive security intelligence, and genomics models dictionaries enable relationship extraction; n Reveal complex relationships through and the opinion dictionary also enables interactive graphs that show multiple sentiment extraction (such as likes and semantic links between two concepts dislikes). * Features subject to change based on final product release. Symbol indicates a new feature. To learn more, please visit www.spss.com. For SPSS office locations and telephone numbers, go to www.spss.com/worldwide. SPSS is a registered trademark and the other SPSS products named are trademarks of SPSS Inc. All other names are trademarks of their respective owners. © 2007 SPSS Inc. All rights reserved. TMC5SPCA4-0407