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▐██▌ September 2017
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█ Text Analytics
█
▄
█ by Bill Inmon
█ Publisher: Technics Publications
▓ Release Date: September 2017
▒ Running time: 2:38:22
▒ Topic: Analytics
░
░ Size: 08x50mb
░ Course Description: Hear the legendary Bill Inmon discuss text
analytics. Not only will Bill teach you about
textual analytics, he will give you exercises and
explain the answers as if you are in the classroom
with him! Over a dozen exercises are included in
this video.
Text is everywhere in the corporation. Yet
corporations use only a paltry amount of it. In
todayÆs world, 95% or more of corporate decisions
are made based on classical structured data. Yet
there is a wealth of information locked up in text.
So what is the problem with text? There are many
challenges. But the primary challenge is that text
does not fit comfortably or well with standard
database structure. Standard database structures
require data to be nice and uniform. But text is
anything but uniform. And there are other important
challenges with text. Language is inherently
complex. And processing text requires more than just
the handling of text. Managing text requires the
identification and management of the context of text
as well.
This overview course examines the challenges facing
the organization that wishes to incorporate text
into the decision making process. This overview
course contains both lecture and interactive
activities.
Upon completing this course the viewer will be able
to identify the activities that must be done in
order to start using text in management decisions.
Clips covered include:
Quantifying Business Value. Most business decisions
are made based upon structured data, yet most of the
data in the corporation is unstructured/textual. The
benefits for each type of unstructured data is
discussed, including the voice of the customer. The
challenges of analyzing text are covered, as well as
some statistics around the quantity of business
decisions made based upon structured versus
unstructured data.
Visualizing Text. We will explore transforming raw
text into a visual that management can use to make
important corporate decisions.
Identifying the Correct Audience. Learn who receives
the benefits of textual analytics (HINT: it is not
the IT department!). Marketing, Sales, Finance, and
Management use cases will be discussed.
More on Visualizations. The benefits of
visualization are discussed with many examples,
including those involving demographics and
geography.
Iterative Processing. We discuss the process for
working with text, starting with capturing raw data,
then creating taxonomies, preparing textual
disambiguation technology, building databases, and
finally visualization.
Acquiring Text. Text comes from many different
places, including voice communication, paper
documents, telephone transcriptions, and email
conversions. We discuss these different sources
along with the challenges they raise.
Formatting Raw Text. Several examples are provided,
showing how to go from raw text into something more
useful.
Deciphering a Taxonomy. A taxonomy is defined and
several examples are provided.
Categorizing Taxonomies. We explore the two main
categories of taxonomies (language and industry-
specific), and give examples of each.
Leveraging Taxonomies. Taxonomies help us organize
text. They help us identify the important words and
form the foundation of sentiment analysis.
Generalizing Taxonomies. The same or very similar
taxonomies apply to different companies in the same
industry. We go through a number of examples.
Editing Text Basics. We cover the basic ways to do
textual disambiguation, including stop word
processing, alternate spelling, acronym resolution,
and stemming.
Resolving Taxonomies. The process of matching your
taxonomies against your documents is called
ôtaxonomy resolutionö. We discuss taxonomy
resolution and provide several examples.
Exploring the Database. We discuss the standard
relational record and how text is transformed to fit
into the relational database.
Post Processing. We discuss post processing, which
are the steps in going from a traditional relational
database to an analytical database. Some of the
functions discussed include inference processing,
conjunctions, and negations.
Processing Sentiment Analysis. We discuss going from
the analytic database to visualzation using several
examples of sentiment analysis.
Leveraging Textual Analytics. Several examples
including a call center example illustrate the value
of textual analytics.
Improving on Textual Analytics. We discuss the
evolution of textual analytics and how it has
improved over the years, such as through soundex and
stemming.