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Statisticians, business analysts, and market researchers who incorporate free-format textual information in their analyses; managers of large document collections who must organize and select documents using data mining; and students of data mining who want to learn about text mining
• Convert documents stored in standard formats (Microsoft Word, Adobe PDF, and so on) into general purpose HTML or TXT formats
• Read documents from a variety of sources (web pages, flat files, data elements in a relational database, spreadsheet cells, and so on) into SAS tables
• Process textual data for text mining (for example, correct misspellings or recode acronyms and abbreviations)
• Convert unstructured text-based character data into structured numeric data
• Explore words and phrases in a document collection
• Query document collections using keywords (that is, identify documents having specific words or phrases)
• Identify topics or concepts that appear in a document collection
• Create user-influenced topic tables from scratch or by modifying machine generated topics or concepts using domain knowledge
• Use derived topic tables or pre-existing user-influenced topic tables (or both) to enhance information retrieval and document classification
• Cluster documents into homogeneous subgroups
• Classify documents into predefined categories
Module 1: Introduction to SAS Enterprise Miner and SAS Text Miner
Module 2: Overview of Text Analytics
Module 3: Algorithmic and Methodological Considerations in Text Mining
Module 4: Additional Ideas and Nodes
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