By Steven Struhl
Bridging the space among the marketer who needs to positioned textual content analytics to take advantage of and knowledge research specialists, Practical textual content Analytics is an available consultant to the various advances in textual content analytics. It explains the various techniques and techniques, their makes use of, strengths, and weaknesses, in a manner that's suitable to advertising and marketing professionals. every one bankruptcy contains illustrations and charts, tricks and assistance, tips on the instruments and strategies, 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 arrange the complicated array of equipment, body the precise questions, and observe the consequences effectively to discover which means in any unstructured information and strengthen powerful new advertising and marketing options.
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Extra resources for Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence
Example text
8e02902. jpg. All the methods we explain work on text you have gathered in a database or a file. You can, of course, go back and gather text frequently, but the text needs to be stored and settled before the analytical methods we discuss can work. It also takes time to reach good answers with the more advanced analytical methods, particularly ones where we look for likely influences on outcomes such as liking, preference, time spent on a web page or shopping and buying behaviour. Good analytics never are instant.
When a result is significant, it means you are very confident that you are not making a false claim. Significance does not measure how likely you are to be missing something real, which is determined by the much less-used statistical power. Testing for significance tends to break down with huge samples or with hundreds of comparisons. You need to use the test of what is sensible along with statistical significance testing. Perhaps you recall the term null hypothesis. In non-formal terms, this means the belief that nothing is happening.
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.