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Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence
In an age where customer opinion and feedback can have an immediate, major effect upon the success of a business or organization, marketers must have the ability to analyze unstructured data in everything from social media and internet reviews to customer surveys and phone logs. Practical Text Analytics is an essential daily reference resource, providing real-world guidance on the effective application of text analytics. The book presents the analysis process so that it is immediately understood by the marketing professionals who must use it, so they can apply proven concepts and methods correctly and with confidence.
By decoding industry terminology and demonstrating practical application of data models once reserved for experts, Practical Text Analytics shows marketers how to frame the right questions, identify key themes and find hidden meaning from unstructured data. Readers will learn to develop powerful new marketing strategies to elevate customer experience, solidify brand value and elevate reputation. Online resources include self-test questions, chapter review Q&A and an Instructor's Manual with text sources and instructions.
By decoding industry terminology and demonstrating practical application of data models once reserved for experts, Practical Text Analytics shows marketers how to frame the right questions, identify key themes and find hidden meaning from unstructured data. Readers will learn to develop powerful new marketing strategies to elevate customer experience, solidify brand value and elevate reputation. Online resources include self-test questions, chapter review Q&A and an Instructor's Manual with text sources and instructions.
272 pages, Paperback
First published July 3, 2015
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Displaying 1 - 2 of 2 reviews
August 18, 2015
Here is a fascinating, if not rather specialist book, which aims to be an accessible guide to the world of text analytics and data analysis for marketing folk.
As an upmarket “how-to guide”, the author has sought to make this a jargon-restricted zone, showing working methodologies and actionable strategies for the use of text analytics without dumbing it down to an absurd, useless level. As the book’s marketing materials note, the book: “…presents the process of analysis in ways that people who use the data need to see them, helping marketers to clarify and organise confidently the confusing array of methods, frame the right questions and apply the results successfully to find meaning in any unstructured data and develop powerful new marketing strategies.”
This book may make your head ache at first if this subject is entirely new, yet just think how even more difficult it could be if it did not have the benefit of the author’s simple, sympathetic and at times humorous treatment to make it so reader-friendly! The author aims to get you understanding how text analytics can be used within your organisation, enabling you to perhaps have a go yourself as well as liaising with programmers and external vendors who may use a strange language that vaguely resembles English (or your country’s language) peppered in with a lot of mysterious jargon for good measure.
Text that you could, should or may need to analyse can be everywhere and a lot of it, most of it, may be unstructured. Dependent on your needs a lot of this text can yield insights into what people might be thinking, feeling or saying about your company and its products. It might even allow you to guess what they are looking for and not finding. The world can literally at your fingertips, as long as you suck in enough data and manage to sufficiently analyse it.
Are you still confused? It does not help that terms such as data and information may be used interchangeably. The author seeks to clarify matters, saying: “Anything that can be collected in any fashion counts as data. Noise from a cell phone tower is data. Mistranslated text is data. Propaganda is data. Data exists, but may not have any purpose. It is not information. The distinction is essential. Information conveys valuable new insights, telling us about something that we did not expect, or allowing us to deal with an unusual contingency. We often encounter the belief that having a great deal of data also means that we must have something interesting. Some writers have even made a point of saying, apparently earnestly, that more data is always better. This is not true. In many cases, this assertion can be completely backward. If the data you have at hand mainly is not useful, more of it makes it harder for you to find what you need, not easier. As valuable items get overrun by useless ones, they become more elusive, and may even vanish from sight entirely. Knowing where to look, as well as how to do the analysis, becomes critical with data that is not structured.”
There is a phrase about “not seeing the wood for the trees” in English that can be applied here. The author hopes that you will be able to see right through the forest of text after applying a “filter” with the help of text analytics, making an information pathway through the forest of noise as you go.
Certainly this seems to be a valiant, great attempt at demystifying a very complex being. Should you have a need for this book, the author might be your literary knight in shining armour.
Practical Text Analytics, written by Steven Struhl and published by Kogan Page. ISBN 9780749474010. YYYY
Autamme.com
As an upmarket “how-to guide”, the author has sought to make this a jargon-restricted zone, showing working methodologies and actionable strategies for the use of text analytics without dumbing it down to an absurd, useless level. As the book’s marketing materials note, the book: “…presents the process of analysis in ways that people who use the data need to see them, helping marketers to clarify and organise confidently the confusing array of methods, frame the right questions and apply the results successfully to find meaning in any unstructured data and develop powerful new marketing strategies.”
This book may make your head ache at first if this subject is entirely new, yet just think how even more difficult it could be if it did not have the benefit of the author’s simple, sympathetic and at times humorous treatment to make it so reader-friendly! The author aims to get you understanding how text analytics can be used within your organisation, enabling you to perhaps have a go yourself as well as liaising with programmers and external vendors who may use a strange language that vaguely resembles English (or your country’s language) peppered in with a lot of mysterious jargon for good measure.
Text that you could, should or may need to analyse can be everywhere and a lot of it, most of it, may be unstructured. Dependent on your needs a lot of this text can yield insights into what people might be thinking, feeling or saying about your company and its products. It might even allow you to guess what they are looking for and not finding. The world can literally at your fingertips, as long as you suck in enough data and manage to sufficiently analyse it.
Are you still confused? It does not help that terms such as data and information may be used interchangeably. The author seeks to clarify matters, saying: “Anything that can be collected in any fashion counts as data. Noise from a cell phone tower is data. Mistranslated text is data. Propaganda is data. Data exists, but may not have any purpose. It is not information. The distinction is essential. Information conveys valuable new insights, telling us about something that we did not expect, or allowing us to deal with an unusual contingency. We often encounter the belief that having a great deal of data also means that we must have something interesting. Some writers have even made a point of saying, apparently earnestly, that more data is always better. This is not true. In many cases, this assertion can be completely backward. If the data you have at hand mainly is not useful, more of it makes it harder for you to find what you need, not easier. As valuable items get overrun by useless ones, they become more elusive, and may even vanish from sight entirely. Knowing where to look, as well as how to do the analysis, becomes critical with data that is not structured.”
There is a phrase about “not seeing the wood for the trees” in English that can be applied here. The author hopes that you will be able to see right through the forest of text after applying a “filter” with the help of text analytics, making an information pathway through the forest of noise as you go.
Certainly this seems to be a valiant, great attempt at demystifying a very complex being. Should you have a need for this book, the author might be your literary knight in shining armour.
Practical Text Analytics, written by Steven Struhl and published by Kogan Page. ISBN 9780749474010. YYYY
Autamme.com
December 4, 2015
This is a great introduction to text analytics. It is highly accessible since it's not heavy on the math but it gives you just enough technical information in order to know what's being done.
You don't have to be a machine learning expert to read this one, in fact every concept you need is explained in the book.
You don't have to be a machine learning expert to read this one, in fact every concept you need is explained in the book.
Displaying 1 - 2 of 2 reviews