By Steven Struhl
Bridging the distance among the marketer who needs to positioned textual content analytics to exploit and information research specialists, Practical textual content Analytics is an obtainable advisor to the numerous advances in textual content analytics. It explains the various techniques and techniques, their makes use of, strengths, and weaknesses, in a manner that's appropriate to advertising professionals. each one bankruptcy contains illustrations and charts, tricks and counsel, tips about the instruments and methods, definitions, and case studies/examples.
Consultant and researcher Steven Struhl presents the method of textual content research in ways in which will help marketers make clear and set up the complicated array of tools, body the perfect questions, and follow the consequences effectively to discover that means in any unstructured info and boost powerful new advertising techniques.
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Extra resources for Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence
The software recognizes phrases in the dictionary as single items. This is a step that not all text analytics software takes. Some more expensive programs, such as SAS Text Miner and SPSS Text Analytics recognize phrases as well as single words. (SAS and SPSS are well-established programs that are very well known to data analysts and statisticians of all stripes. ) The free programs Apache NLP (Natural Language Processing) and RapidMiner can do this as well. The SPSS program, which we will discuss more fully, also encodes text, grouping comments by topic heading.
Large or small, it is one block of text we want to examine as a unit. We could gather the comments of any number of people into a single document, but that is not usually done. A document most simply holds the comments made by one person or otherwise is a single collection of text. The word vector model captures the text in a document as a collection of words on a single row of a spreadsheet or other database, without reference to word order or syntax. 1, we see whether the word was used by each person or not.
Berners-Lee has advocated that the internet should evolve towards being a web of analysable data, meaning that the text it contains would need to accumulate a great deal of other information, largely hidden from the reader, that makes the text behave more like data. (We will talk more about this idea in Chapter 5). Until this massive transformation happens, though, the internet will continue as a largely unstructured mass. Therefore, as the information it contains grows rapidly, it paradoxically will remain difficult to tap and use.