Why is the popularity of text mining as a bI tool increasing? Text mining as a bi tool is increasing because of the rapid growth in text data and availability of sophisticated BI tools. The benefits of text mining are obvious in the areas where very large amounts of textual data are being generated, such as law(court orders), academic research(research articles), finance( quarterly technology(patent files), and marketing(customer comments)actions) reports), medicine(discharge summaries), biology(molecular interactions) What are some popular application areas of text mining? within text by looking for predefined sequences in text via pattern matching Topic tracking. Based on a user profile and documents that a use text mining can predict other documents of interest to the user er views, Summarization. Summarizing a document to save time on the part of the Categorization. Identifying the main themes of a document and then placing the document into a predefined set of categories based on those themes Clustering Grouping similar documents without having a predefined set of categories Concept linking. Connects related documents by identifying their shared concepts and, by doing So, helps users find information that they perhaps would not have found using trad itional search methods Question answering. Finding the best answer to a given question through knowledge-driven pattern matchin Section 5.3 Review Questions What is NLP? Natural language processing(NLP)is an important component of text mining and is a subfield of artificial intelligence and computational linguistics. It studies the problem of"understand ing" the natural human language, with the view of converting depictions of human language(such as textual documents)into more formal representations(in the form of numeric and symbolic data)that are easier for computer programs to manipulate Copyright C2018 Pearson Education, Inc.4 Copyright © 2018Pearson Education, Inc. 3. Why is the popularity of text mining as a BI tool increasing? Text mining as a BI tool is increasing because of the rapid growth in text data and availability of sophisticated BI tools. The benefits of text mining are obvious in the areas where very large amounts of textual data are being generated, such as law (court orders), academic research (research articles), finance (quarterly reports), medicine (discharge summaries), biology (molecular interactions), technology (patent files), and marketing (customer comments). 4. What are some popular application areas of text mining? • Information extraction. Identification of key phrases and relationships within text by looking for predefined sequences in text via pattern matching. • Topic tracking. Based on a user profile and documents that a user views, text mining can predict other documents of interest to the user. • Summarization. Summarizing a document to save time on the part of the reader. • Categorization. Identifying the main themes of a document and then placing the document into a predefined set of categories based on those themes. • Clustering. Grouping similar documents without having a predefined set of categories. • Concept linking. Connects related documents by identifying their shared concepts and, by doing so, helps users find information that they perhaps would not have found using traditional search methods. • Question answering. Finding the best answer to a given question through knowledge-driven pattern matching. Section 5.3 Review Questions 1. What is NLP? Natural language processing (NLP) is an important component of text mining and is a subfield of artificial intelligence and computational linguistics. It studies the problem of “understanding” the natural human language, with the view of converting depictions of human language (such as textual documents) into more formal representations (in the form of numeric and symbolic data) that are easier for computer programs to manipulate